dataset
stringclasses 6
values | model
stringclasses 13
values | seed
int64 42
46
| n_topics
int64 10
50
| topic_descriptions
sequencelengths 1
50
| runtime_s
float64 0.93
52.3k
| encoder
stringclasses 5
values | diversity
float64 0.09
1
| c_npmi
float64 -0.38
0.21
| wec_ex
float64 0.11
0.49
| wec_in
float64 0.07
0.94
|
---|---|---|---|---|---|---|---|---|---|---|
ArXiv ML Papers | BERTopic | 43 | 10 | [
[
"with",
"of",
"we",
"for",
"in",
"and",
"the",
"that",
"is",
"to"
],
[
"to",
"the",
"privacy",
"federated",
"learning",
"of",
"data",
"and",
"in",
"we"
],
[
"in",
"of",
"that",
"the",
"and",
"to",
"we",
"on",
"this",
"for"
],
[
"to",
"time",
"the",
"and",
"of",
"series",
"in",
"for",
"forecasting",
"is"
],
[
"of",
"we",
"the",
"in",
"graph",
"graphs",
"to",
"and",
"on",
"node"
],
[
"and",
"the",
"image",
"in",
"of",
"we",
"to",
"for",
"on",
"with"
],
[
"we",
"of",
"the",
"to",
"in",
"and",
"learning",
"that",
"policy",
"is"
],
[
"and",
"eeg",
"the",
"to",
"of",
"signals",
"in",
"on",
"data",
"using"
],
[
"to",
"and",
"is",
"protein",
"in",
"the",
"of",
"side",
"health",
"for"
],
[
"the",
"to",
"fairness",
"and",
"of",
"fair",
"we",
"that",
"in",
"learning"
]
] | 19.087417 | all-MiniLM-L6-v2 | 0.32 | -0.028712 | 0.232479 | 0.556292 |
ArXiv ML Papers | BERTopic | 44 | 10 | [
[
"we",
"of",
"in",
"and",
"the",
"that",
"is",
"for",
"to",
"on"
],
[
"to",
"of",
"the",
"in",
"and",
"we",
"that",
"for",
"is",
"on"
],
[
"and",
"fairness",
"of",
"we",
"in",
"to",
"the",
"that",
"fair",
"learning"
],
[
"the",
"to",
"in",
"eeg",
"on",
"of",
"signals",
"and",
"data",
"as"
],
[
"of",
"graph",
"the",
"and",
"to",
"in",
"graphs",
"we",
"on",
"node"
],
[
"we",
"in",
"the",
"of",
"and",
"for",
"image",
"to",
"with",
"on"
],
[
"attacks",
"to",
"the",
"in",
"that",
"adversarial",
"and",
"we",
"attack",
"of"
],
[
"of",
"the",
"to",
"learning",
"in",
"and",
"we",
"that",
"policy",
"is"
],
[
"treatment",
"causal",
"the",
"of",
"to",
"in",
"we",
"is",
"and",
"outcome"
],
[
"to",
"learning",
"federated",
"the",
"devices",
"data",
"and",
"of",
"in",
"server"
]
] | 14.536075 | all-MiniLM-L6-v2 | 0.32 | -0.025437 | 0.23001 | 0.543245 |
ArXiv ML Papers | BERTopic | 45 | 10 | [
[
"on",
"in",
"we",
"the",
"and",
"of",
"for",
"to",
"that",
"is"
],
[
"for",
"of",
"image",
"to",
"on",
"the",
"we",
"and",
"in",
"with"
],
[
"the",
"privacy",
"federated",
"to",
"and",
"learning",
"data",
"we",
"of",
"in"
],
[
"the",
"in",
"network",
"we",
"to",
"neural",
"of",
"and",
"with",
"for"
],
[
"for",
"and",
"to",
"of",
"the",
"in",
"is",
"we",
"data",
"that"
],
[
"to",
"learning",
"and",
"fair",
"the",
"that",
"fairness",
"in",
"we",
"of"
],
[
"of",
"variational",
"we",
"to",
"in",
"the",
"and",
"bayesian",
"is",
"inference"
],
[
"eeg",
"and",
"in",
"to",
"signals",
"of",
"data",
"the",
"on",
"as"
],
[
"to",
"attacks",
"and",
"adversarial",
"the",
"of",
"we",
"attack",
"in",
"that"
],
[
"of",
"in",
"the",
"to",
"and",
"we",
"learning",
"that",
"policy",
"is"
]
] | 14.676268 | all-MiniLM-L6-v2 | 0.3 | -0.026364 | 0.224854 | 0.539863 |
ArXiv ML Papers | BERTopic | 46 | 10 | [
[
"the",
"of",
"to",
"time",
"and",
"in",
"for",
"data",
"series",
"is"
],
[
"to",
"the",
"and",
"of",
"in",
"for",
"data",
"learning",
"we",
"is"
],
[
"attacks",
"we",
"in",
"adversarial",
"of",
"and",
"that",
"the",
"training",
"to"
],
[
"neural",
"for",
"is",
"to",
"the",
"and",
"in",
"we",
"of",
"with"
],
[
"image",
"for",
"and",
"we",
"to",
"of",
"in",
"the",
"on",
"with"
],
[
"to",
"and",
"the",
"of",
"graph",
"we",
"in",
"on",
"that",
"graphs"
],
[
"and",
"of",
"learning",
"we",
"that",
"to",
"in",
"the",
"policy",
"is"
],
[
"to",
"and",
"in",
"of",
"speech",
"the",
"we",
"audio",
"for",
"on"
],
[
"fair",
"fairness",
"the",
"that",
"and",
"to",
"of",
"we",
"in",
"learning"
],
[
"and",
"of",
"the",
"to",
"is",
"in",
"for",
"we",
"algorithm",
"matrix"
]
] | 14.212026 | all-MiniLM-L6-v2 | 0.29 | -0.030181 | 0.222051 | 0.532827 |
ArXiv ML Papers | BERTopic | 43 | 20 | [
[
"in",
"with",
"the",
"image",
"segmentation",
"of",
"for",
"to",
"we",
"and"
],
[
"and",
"image",
"to",
"gans",
"gan",
"of",
"images",
"generative",
"the",
"we"
],
[
"3d",
"scene",
"and",
"point",
"the",
"semantic",
"to",
"depth",
"we",
"in"
],
[
"to",
"data",
"of",
"in",
"the",
"and",
"is",
"for",
"we",
"matrix"
],
[
"gradient",
"the",
"stochastic",
"of",
"convex",
"we",
"and",
"convergence",
"is",
"optimization"
],
[
"the",
"in",
"pruning",
"network",
"to",
"of",
"on",
"neural",
"and",
"hardware"
],
[
"and",
"in",
"we",
"equations",
"to",
"of",
"neural",
"the",
"physics",
"data"
],
[
"of",
"time",
"the",
"and",
"forecasting",
"series",
"to",
"for",
"in",
"model"
],
[
"and",
"the",
"variational",
"of",
"to",
"we",
"in",
"for",
"is",
"bayesian"
],
[
"and",
"graphs",
"graph",
"the",
"of",
"to",
"in",
"nodes",
"node",
"we"
],
[
"in",
"we",
"and",
"models",
"to",
"software",
"of",
"the",
"explanation",
"explanations"
],
[
"to",
"and",
"the",
"of",
"in",
"for",
"on",
"speech",
"language",
"we"
],
[
"private",
"data",
"privacy",
"differentially",
"the",
"to",
"we",
"that",
"learning",
"our"
],
[
"federated",
"to",
"distributed",
"learning",
"the",
"devices",
"data",
"and",
"server",
"communication"
],
[
"the",
"learning",
"of",
"in",
"to",
"and",
"policy",
"we",
"that",
"is"
],
[
"the",
"attacks",
"to",
"of",
"adversarial",
"attack",
"we",
"training",
"in",
"and"
],
[
"to",
"on",
"eeg",
"signals",
"of",
"the",
"and",
"in",
"using",
"data"
],
[
"side",
"protein",
"the",
"and",
"of",
"to",
"health",
"in",
"is",
"patient"
],
[
"neural",
"the",
"network",
"we",
"of",
"networks",
"relu",
"in",
"to",
"layer"
],
[
"fairness",
"the",
"to",
"we",
"of",
"and",
"fair",
"that",
"learning",
"machine"
]
] | 15.018337 | all-MiniLM-L6-v2 | 0.395 | -0.00707 | 0.172834 | 0.582825 |
ArXiv ML Papers | BERTopic | 44 | 20 | [
[
"segmentation",
"the",
"and",
"to",
"in",
"of",
"with",
"image",
"for",
"we"
],
[
"for",
"and",
"the",
"of",
"we",
"uncertainty",
"to",
"variational",
"in",
"is"
],
[
"of",
"to",
"the",
"pruning",
"and",
"neural",
"network",
"hardware",
"on",
"nas"
],
[
"image",
"gans",
"gan",
"the",
"generative",
"to",
"images",
"of",
"generator",
"and"
],
[
"of",
"for",
"the",
"in",
"is",
"and",
"we",
"to",
"matrix",
"data"
],
[
"protein",
"of",
"to",
"health",
"the",
"and",
"in",
"predict",
"is",
"this"
],
[
"class",
"classification",
"the",
"decision",
"of",
"classifier",
"label",
"to",
"noise",
"codes"
],
[
"of",
"eeg",
"to",
"and",
"the",
"signals",
"in",
"on",
"data",
"as"
],
[
"the",
"gradient",
"of",
"stochastic",
"convex",
"and",
"is",
"we",
"optimization",
"convergence"
],
[
"of",
"the",
"graph",
"and",
"graphs",
"we",
"node",
"to",
"in",
"on"
],
[
"differential",
"to",
"that",
"privacy",
"private",
"the",
"differentially",
"data",
"we",
"of"
],
[
"model",
"of",
"time",
"series",
"in",
"for",
"forecasting",
"to",
"the",
"and"
],
[
"to",
"fair",
"of",
"and",
"fairness",
"the",
"we",
"that",
"machine",
"learning"
],
[
"and",
"on",
"of",
"we",
"for",
"that",
"to",
"in",
"the",
"language"
],
[
"adversarial",
"the",
"attacks",
"to",
"training",
"and",
"of",
"we",
"in",
"attack"
],
[
"we",
"in",
"learning",
"to",
"the",
"policy",
"that",
"of",
"and",
"is"
],
[
"the",
"to",
"of",
"and",
"neural",
"we",
"equations",
"physics",
"in",
"data"
],
[
"treatment",
"in",
"the",
"outcome",
"we",
"causal",
"of",
"to",
"observational",
"is"
],
[
"of",
"relu",
"in",
"to",
"network",
"networks",
"neural",
"the",
"we",
"that"
],
[
"data",
"to",
"federated",
"training",
"distributed",
"server",
"learning",
"devices",
"the",
"and"
]
] | 14.292851 | all-MiniLM-L6-v2 | 0.395 | -0.009121 | 0.174477 | 0.578752 |
ArXiv ML Papers | BERTopic | 45 | 20 | [
[
"we",
"policy",
"that",
"learning",
"in",
"the",
"and",
"of",
"to",
"is"
],
[
"and",
"fairness",
"fair",
"to",
"we",
"that",
"of",
"the",
"machine",
"learning"
],
[
"we",
"the",
"in",
"of",
"neural",
"to",
"and",
"for",
"data",
"physics"
],
[
"federated",
"training",
"learning",
"and",
"data",
"devices",
"to",
"server",
"the",
"distributed"
],
[
"to",
"we",
"the",
"privacy",
"private",
"differentially",
"data",
"that",
"differential",
"learning"
],
[
"attacks",
"of",
"the",
"adversarial",
"to",
"attack",
"we",
"and",
"in",
"that"
],
[
"the",
"we",
"that",
"and",
"in",
"language",
"on",
"to",
"for",
"of"
],
[
"signals",
"eeg",
"the",
"to",
"of",
"and",
"on",
"in",
"data",
"as"
],
[
"of",
"graph",
"and",
"to",
"the",
"in",
"we",
"graphs",
"on",
"for"
],
[
"the",
"of",
"neural",
"network",
"relu",
"networks",
"in",
"that",
"we",
"to"
],
[
"of",
"time",
"the",
"series",
"forecasting",
"for",
"to",
"and",
"in",
"model"
],
[
"is",
"of",
"for",
"in",
"to",
"and",
"the",
"data",
"we",
"algorithm"
],
[
"generative",
"gans",
"gan",
"image",
"to",
"images",
"and",
"generator",
"the",
"of"
],
[
"the",
"of",
"in",
"we",
"to",
"image",
"and",
"medical",
"segmentation",
"for"
],
[
"pruning",
"to",
"the",
"on",
"of",
"neural",
"and",
"hardware",
"in",
"nas"
],
[
"and",
"in",
"of",
"detection",
"the",
"to",
"we",
"for",
"object",
"on"
],
[
"convex",
"optimization",
"and",
"stochastic",
"gradient",
"convergence",
"of",
"the",
"we",
"is"
],
[
"and",
"variational",
"in",
"we",
"of",
"latent",
"to",
"uncertainty",
"the",
"posterior"
],
[
"the",
"we",
"inference",
"on",
"bayesian",
"for",
"and",
"of",
"to",
"in"
]
] | 37.529427 | all-MiniLM-L6-v2 | 0.357895 | -0.008285 | 0.179466 | 0.583949 |
ArXiv ML Papers | BERTopic | 46 | 20 | [
[
"the",
"of",
"and",
"to",
"in",
"neural",
"network",
"we",
"networks",
"for"
],
[
"of",
"to",
"metric",
"the",
"learning",
"distance",
"data",
"in",
"we",
"is"
],
[
"segmentation",
"of",
"and",
"to",
"the",
"in",
"for",
"image",
"with",
"we"
],
[
"we",
"the",
"point",
"depth",
"and",
"3d",
"of",
"scene",
"to",
"in"
],
[
"algorithm",
"for",
"is",
"in",
"and",
"of",
"the",
"we",
"to",
"matrix"
],
[
"series",
"the",
"time",
"and",
"forecasting",
"of",
"in",
"to",
"for",
"model"
],
[
"and",
"to",
"hardware",
"of",
"pruning",
"neural",
"the",
"on",
"network",
"accuracy"
],
[
"speech",
"of",
"in",
"audio",
"to",
"and",
"the",
"speaker",
"we",
"for"
],
[
"in",
"the",
"for",
"is",
"variational",
"to",
"we",
"and",
"of",
"bayesian"
],
[
"gan",
"image",
"generative",
"the",
"to",
"and",
"images",
"of",
"gans",
"we"
],
[
"the",
"eeg",
"and",
"to",
"of",
"in",
"signals",
"on",
"as",
"using"
],
[
"to",
"and",
"federated",
"learning",
"data",
"devices",
"server",
"the",
"distributed",
"communication"
],
[
"graph",
"graphs",
"of",
"the",
"and",
"in",
"on",
"to",
"node",
"we"
],
[
"in",
"and",
"to",
"of",
"the",
"learning",
"policy",
"we",
"is",
"that"
],
[
"the",
"for",
"in",
"protein",
"of",
"to",
"and",
"are",
"is",
"health"
],
[
"recommendation",
"user",
"to",
"item",
"items",
"recommender",
"of",
"we",
"the",
"and"
],
[
"privacy",
"private",
"sensitive",
"that",
"the",
"attacks",
"to",
"we",
"of",
"differentially"
],
[
"to",
"and",
"we",
"of",
"fairness",
"that",
"the",
"fair",
"machine",
"learning"
],
[
"the",
"to",
"adversarial",
"attacks",
"attack",
"we",
"of",
"and",
"in",
"training"
],
[
"language",
"the",
"to",
"of",
"models",
"and",
"we",
"on",
"in",
"for"
]
] | 13.924605 | all-MiniLM-L6-v2 | 0.38 | -0.006415 | 0.178178 | 0.564837 |
ArXiv ML Papers | BERTopic | 43 | 30 | [
[
"distributed",
"the",
"federated",
"learning",
"to",
"devices",
"data",
"server",
"communication",
"and"
],
[
"we",
"learning",
"of",
"in",
"the",
"to",
"policy",
"and",
"that",
"is"
],
[
"attacks",
"the",
"to",
"attack",
"adversarial",
"of",
"and",
"we",
"in",
"training"
],
[
"that",
"data",
"privacy",
"differentially",
"the",
"private",
"we",
"to",
"learning",
"differential"
],
[
"fairness",
"and",
"to",
"fair",
"the",
"of",
"machine",
"we",
"that",
"learning"
],
[
"to",
"the",
"and",
"of",
"speech",
"language",
"in",
"we",
"for",
"on"
],
[
"in",
"we",
"and",
"models",
"to",
"software",
"of",
"the",
"explanation",
"explanations"
],
[
"and",
"of",
"the",
"to",
"neural",
"we",
"physics",
"equations",
"in",
"data"
],
[
"network",
"the",
"neural",
"networks",
"relu",
"of",
"we",
"in",
"layer",
"to"
],
[
"to",
"on",
"eeg",
"signals",
"of",
"the",
"and",
"in",
"using",
"data"
],
[
"the",
"graphs",
"graph",
"of",
"and",
"nodes",
"node",
"to",
"we",
"in"
],
[
"side",
"protein",
"the",
"and",
"of",
"to",
"health",
"in",
"patient",
"is"
],
[
"time",
"the",
"of",
"and",
"forecasting",
"to",
"series",
"in",
"for",
"model"
],
[
"gradient",
"convex",
"and",
"of",
"the",
"we",
"stochastic",
"convergence",
"is",
"optimization"
],
[
"and",
"the",
"variational",
"of",
"to",
"we",
"in",
"for",
"is",
"bayesian"
],
[
"of",
"for",
"the",
"is",
"and",
"in",
"to",
"data",
"matrix",
"we"
],
[
"images",
"image",
"gans",
"gan",
"generative",
"to",
"the",
"and",
"of",
"we"
],
[
"of",
"and",
"the",
"pruning",
"to",
"neural",
"on",
"hardware",
"network",
"in"
],
[
"the",
"segmentation",
"of",
"and",
"to",
"in",
"image",
"medical",
"we",
"for"
],
[
"and",
"with",
"the",
"object",
"to",
"in",
"detection",
"of",
"on",
"we"
],
[
"we",
"and",
"depth",
"scene",
"the",
"3d",
"point",
"to",
"semantic",
"in"
]
] | 11.780045 | all-MiniLM-L6-v2 | 0.390476 | -0.006637 | 0.170969 | 0.576168 |
ArXiv ML Papers | BERTopic | 44 | 30 | [
[
"we",
"in",
"learning",
"to",
"the",
"policy",
"that",
"of",
"and",
"is"
],
[
"to",
"fair",
"of",
"and",
"fairness",
"the",
"we",
"that",
"machine",
"learning"
],
[
"data",
"federated",
"server",
"to",
"distributed",
"devices",
"learning",
"and",
"the",
"training"
],
[
"we",
"to",
"of",
"attacks",
"adversarial",
"the",
"attack",
"and",
"in",
"training"
],
[
"data",
"private",
"we",
"the",
"that",
"privacy",
"differential",
"differentially",
"to",
"sensitive"
],
[
"of",
"the",
"causal",
"outcome",
"treatment",
"to",
"we",
"in",
"is",
"observational"
],
[
"and",
"on",
"of",
"we",
"for",
"that",
"to",
"in",
"the",
"language"
],
[
"the",
"of",
"and",
"to",
"neural",
"we",
"physics",
"equations",
"in",
"data"
],
[
"the",
"forecasting",
"time",
"series",
"and",
"in",
"of",
"to",
"for",
"model"
],
[
"network",
"relu",
"in",
"to",
"the",
"of",
"neural",
"networks",
"we",
"that"
],
[
"the",
"eeg",
"to",
"signals",
"and",
"of",
"in",
"on",
"data",
"as"
],
[
"to",
"health",
"and",
"protein",
"the",
"of",
"in",
"is",
"predict",
"machine"
],
[
"of",
"the",
"graph",
"and",
"graphs",
"we",
"node",
"to",
"in",
"nodes"
],
[
"classification",
"label",
"the",
"decision",
"of",
"classifier",
"class",
"to",
"noise",
"codes"
],
[
"in",
"and",
"for",
"of",
"the",
"is",
"to",
"matrix",
"we",
"data"
],
[
"and",
"of",
"the",
"gradient",
"convex",
"stochastic",
"we",
"is",
"optimization",
"convergence"
],
[
"image",
"gans",
"gan",
"generative",
"to",
"images",
"of",
"the",
"generator",
"and"
],
[
"for",
"and",
"the",
"of",
"we",
"uncertainty",
"to",
"variational",
"in",
"is"
],
[
"to",
"the",
"pruning",
"and",
"of",
"hardware",
"network",
"neural",
"on",
"nas"
],
[
"in",
"the",
"with",
"object",
"of",
"on",
"and",
"pooling",
"detection",
"to"
],
[
"and",
"segmentation",
"to",
"of",
"the",
"in",
"image",
"for",
"we",
"with"
]
] | 11.743433 | all-MiniLM-L6-v2 | 0.395238 | -0.009103 | 0.171947 | 0.583365 |
ArXiv ML Papers | BERTopic | 45 | 30 | [
[
"we",
"policy",
"that",
"learning",
"in",
"the",
"and",
"of",
"to",
"is"
],
[
"and",
"fairness",
"fair",
"to",
"we",
"that",
"of",
"the",
"machine",
"learning"
],
[
"we",
"the",
"in",
"of",
"neural",
"to",
"and",
"for",
"data",
"physics"
],
[
"federated",
"training",
"learning",
"and",
"data",
"devices",
"to",
"server",
"the",
"distributed"
],
[
"to",
"we",
"the",
"privacy",
"private",
"differentially",
"data",
"that",
"differential",
"learning"
],
[
"attacks",
"of",
"the",
"adversarial",
"to",
"attack",
"we",
"and",
"in",
"that"
],
[
"the",
"we",
"that",
"and",
"in",
"language",
"on",
"to",
"for",
"of"
],
[
"signals",
"eeg",
"the",
"to",
"of",
"and",
"on",
"in",
"data",
"as"
],
[
"of",
"graph",
"and",
"to",
"the",
"in",
"we",
"graphs",
"on",
"for"
],
[
"the",
"of",
"neural",
"network",
"relu",
"networks",
"in",
"that",
"we",
"to"
],
[
"of",
"time",
"the",
"series",
"forecasting",
"for",
"to",
"and",
"in",
"model"
],
[
"is",
"of",
"for",
"in",
"to",
"and",
"the",
"data",
"we",
"algorithm"
],
[
"generative",
"gans",
"gan",
"image",
"to",
"images",
"and",
"generator",
"the",
"of"
],
[
"the",
"of",
"in",
"we",
"to",
"image",
"and",
"medical",
"segmentation",
"for"
],
[
"pruning",
"to",
"the",
"on",
"of",
"neural",
"and",
"hardware",
"in",
"nas"
],
[
"and",
"in",
"of",
"detection",
"the",
"to",
"we",
"for",
"object",
"on"
],
[
"convex",
"optimization",
"and",
"stochastic",
"gradient",
"convergence",
"of",
"the",
"we",
"is"
],
[
"and",
"variational",
"in",
"we",
"of",
"latent",
"to",
"uncertainty",
"the",
"posterior"
],
[
"the",
"we",
"inference",
"on",
"bayesian",
"for",
"and",
"of",
"to",
"in"
]
] | 11.415018 | all-MiniLM-L6-v2 | 0.357895 | -0.008285 | 0.179466 | 0.564226 |
ArXiv ML Papers | BERTopic | 46 | 30 | [
[
"learning",
"policy",
"in",
"of",
"the",
"we",
"and",
"to",
"that",
"reinforcement"
],
[
"learning",
"federated",
"the",
"to",
"data",
"devices",
"server",
"and",
"distributed",
"communication"
],
[
"that",
"the",
"and",
"of",
"we",
"to",
"fairness",
"fair",
"machine",
"learning"
],
[
"adversarial",
"attacks",
"attack",
"the",
"of",
"we",
"to",
"and",
"in",
"training"
],
[
"attacks",
"differentially",
"sensitive",
"that",
"private",
"privacy",
"the",
"to",
"differential",
"of"
],
[
"to",
"of",
"physics",
"neural",
"and",
"the",
"we",
"equations",
"in",
"data"
],
[
"to",
"signals",
"the",
"as",
"on",
"and",
"eeg",
"in",
"of",
"using"
],
[
"network",
"neural",
"relu",
"the",
"to",
"activation",
"in",
"of",
"networks",
"layer"
],
[
"protein",
"of",
"and",
"to",
"the",
"in",
"for",
"are",
"health",
"is"
],
[
"and",
"graph",
"of",
"we",
"the",
"graphs",
"to",
"node",
"network",
"in"
],
[
"the",
"user",
"of",
"to",
"item",
"recommender",
"we",
"items",
"recommendation",
"users"
],
[
"series",
"of",
"the",
"time",
"forecasting",
"and",
"to",
"in",
"for",
"model"
],
[
"speech",
"of",
"in",
"audio",
"to",
"and",
"the",
"speaker",
"we",
"for"
],
[
"of",
"models",
"language",
"we",
"to",
"the",
"and",
"on",
"in",
"attention"
],
[
"data",
"the",
"classification",
"label",
"is",
"in",
"of",
"learning",
"we",
"labels"
],
[
"image",
"for",
"of",
"to",
"medical",
"segmentation",
"the",
"and",
"in",
"with"
],
[
"is",
"of",
"the",
"and",
"for",
"to",
"in",
"variational",
"we",
"bayesian"
],
[
"convergence",
"is",
"optimization",
"the",
"stochastic",
"of",
"gradient",
"convex",
"and",
"we"
],
[
"generative",
"image",
"gan",
"images",
"of",
"to",
"gans",
"and",
"the",
"generator"
],
[
"metric",
"distance",
"the",
"to",
"we",
"data",
"of",
"learning",
"in",
"and"
],
[
"the",
"matrix",
"tensor",
"algorithm",
"rank",
"for",
"of",
"is",
"in",
"and"
],
[
"nas",
"pruning",
"to",
"on",
"of",
"and",
"neural",
"the",
"hardware",
"accuracy"
],
[
"the",
"point",
"scene",
"depth",
"and",
"3d",
"we",
"of",
"to",
"semantic"
],
[
"detection",
"object",
"the",
"on",
"and",
"to",
"of",
"in",
"cnn",
"for"
]
] | 12.662092 | all-MiniLM-L6-v2 | 0.425 | 0.002061 | 0.164638 | 0.605135 |
ArXiv ML Papers | BERTopic | 43 | 40 | [
[
"distributed",
"the",
"federated",
"learning",
"to",
"devices",
"data",
"server",
"communication",
"and"
],
[
"we",
"learning",
"of",
"in",
"the",
"to",
"policy",
"and",
"that",
"is"
],
[
"attacks",
"the",
"to",
"attack",
"adversarial",
"of",
"and",
"we",
"in",
"training"
],
[
"that",
"data",
"privacy",
"differentially",
"the",
"private",
"we",
"to",
"learning",
"differential"
],
[
"fairness",
"and",
"to",
"fair",
"the",
"of",
"machine",
"we",
"that",
"learning"
],
[
"to",
"the",
"and",
"of",
"speech",
"language",
"in",
"we",
"for",
"on"
],
[
"in",
"we",
"and",
"models",
"to",
"software",
"of",
"the",
"explanation",
"explanations"
],
[
"and",
"of",
"the",
"to",
"neural",
"we",
"physics",
"equations",
"in",
"data"
],
[
"network",
"the",
"neural",
"networks",
"relu",
"of",
"we",
"in",
"layer",
"to"
],
[
"to",
"on",
"eeg",
"signals",
"of",
"the",
"and",
"in",
"using",
"data"
],
[
"the",
"graphs",
"graph",
"of",
"and",
"nodes",
"node",
"to",
"we",
"in"
],
[
"side",
"protein",
"the",
"and",
"of",
"to",
"health",
"in",
"patient",
"is"
],
[
"time",
"the",
"of",
"and",
"forecasting",
"to",
"series",
"in",
"for",
"model"
],
[
"gradient",
"convex",
"and",
"of",
"the",
"we",
"stochastic",
"convergence",
"is",
"optimization"
],
[
"and",
"the",
"variational",
"of",
"to",
"we",
"in",
"for",
"is",
"bayesian"
],
[
"of",
"for",
"the",
"is",
"and",
"in",
"to",
"data",
"matrix",
"we"
],
[
"images",
"image",
"gans",
"gan",
"generative",
"to",
"the",
"and",
"of",
"we"
],
[
"of",
"and",
"the",
"pruning",
"to",
"neural",
"on",
"hardware",
"network",
"in"
],
[
"the",
"segmentation",
"of",
"and",
"to",
"in",
"image",
"medical",
"we",
"for"
],
[
"and",
"with",
"the",
"object",
"to",
"in",
"detection",
"of",
"on",
"we"
],
[
"we",
"and",
"depth",
"scene",
"the",
"3d",
"point",
"to",
"semantic",
"in"
]
] | 15.499882 | all-MiniLM-L6-v2 | 0.390476 | -0.006637 | 0.170969 | 0.576788 |
ArXiv ML Papers | BERTopic | 44 | 40 | [
[
"we",
"in",
"learning",
"to",
"the",
"policy",
"that",
"of",
"and",
"is"
],
[
"to",
"fair",
"of",
"and",
"fairness",
"the",
"we",
"that",
"machine",
"learning"
],
[
"data",
"federated",
"server",
"to",
"distributed",
"devices",
"learning",
"and",
"the",
"training"
],
[
"we",
"to",
"of",
"attacks",
"adversarial",
"the",
"attack",
"and",
"in",
"training"
],
[
"data",
"private",
"we",
"the",
"that",
"privacy",
"differential",
"differentially",
"to",
"sensitive"
],
[
"of",
"the",
"causal",
"outcome",
"treatment",
"to",
"we",
"in",
"is",
"observational"
],
[
"and",
"on",
"of",
"we",
"for",
"that",
"to",
"in",
"the",
"language"
],
[
"the",
"of",
"and",
"to",
"neural",
"we",
"physics",
"equations",
"in",
"data"
],
[
"the",
"forecasting",
"time",
"series",
"and",
"in",
"of",
"to",
"for",
"model"
],
[
"network",
"relu",
"in",
"to",
"the",
"of",
"neural",
"networks",
"we",
"that"
],
[
"the",
"eeg",
"to",
"signals",
"and",
"of",
"in",
"on",
"data",
"as"
],
[
"to",
"health",
"and",
"protein",
"the",
"of",
"in",
"is",
"predict",
"machine"
],
[
"of",
"the",
"graph",
"and",
"graphs",
"we",
"node",
"to",
"in",
"nodes"
],
[
"classification",
"label",
"the",
"decision",
"of",
"classifier",
"class",
"to",
"noise",
"codes"
],
[
"in",
"and",
"for",
"of",
"the",
"is",
"to",
"matrix",
"we",
"data"
],
[
"and",
"of",
"the",
"gradient",
"convex",
"stochastic",
"we",
"is",
"optimization",
"convergence"
],
[
"image",
"gans",
"gan",
"generative",
"to",
"images",
"of",
"the",
"generator",
"and"
],
[
"for",
"and",
"the",
"of",
"we",
"uncertainty",
"to",
"variational",
"in",
"is"
],
[
"to",
"the",
"pruning",
"and",
"of",
"hardware",
"network",
"neural",
"on",
"nas"
],
[
"in",
"the",
"with",
"object",
"of",
"on",
"and",
"pooling",
"detection",
"to"
],
[
"and",
"segmentation",
"to",
"of",
"the",
"in",
"image",
"for",
"we",
"with"
]
] | 26.208948 | all-MiniLM-L6-v2 | 0.395238 | -0.009103 | 0.171947 | 0.589815 |
ArXiv ML Papers | BERTopic | 45 | 40 | [
[
"we",
"policy",
"that",
"learning",
"in",
"the",
"and",
"of",
"to",
"is"
],
[
"and",
"fairness",
"fair",
"to",
"we",
"that",
"of",
"the",
"machine",
"learning"
],
[
"we",
"the",
"in",
"of",
"neural",
"to",
"and",
"for",
"data",
"physics"
],
[
"federated",
"training",
"learning",
"and",
"data",
"devices",
"to",
"server",
"the",
"distributed"
],
[
"to",
"we",
"the",
"privacy",
"private",
"differentially",
"data",
"that",
"differential",
"learning"
],
[
"attacks",
"of",
"the",
"adversarial",
"to",
"attack",
"we",
"and",
"in",
"that"
],
[
"the",
"we",
"that",
"and",
"in",
"language",
"on",
"to",
"for",
"of"
],
[
"signals",
"eeg",
"the",
"to",
"of",
"and",
"on",
"in",
"data",
"as"
],
[
"of",
"graph",
"and",
"to",
"the",
"in",
"we",
"graphs",
"on",
"for"
],
[
"the",
"of",
"neural",
"network",
"relu",
"networks",
"in",
"that",
"we",
"to"
],
[
"of",
"time",
"the",
"series",
"forecasting",
"for",
"to",
"and",
"in",
"model"
],
[
"is",
"of",
"for",
"in",
"to",
"and",
"the",
"data",
"we",
"algorithm"
],
[
"generative",
"gans",
"gan",
"image",
"to",
"images",
"and",
"generator",
"the",
"of"
],
[
"the",
"of",
"in",
"we",
"to",
"image",
"and",
"medical",
"segmentation",
"for"
],
[
"pruning",
"to",
"the",
"on",
"of",
"neural",
"and",
"hardware",
"in",
"nas"
],
[
"and",
"in",
"of",
"detection",
"the",
"to",
"we",
"for",
"object",
"on"
],
[
"convex",
"optimization",
"and",
"stochastic",
"gradient",
"convergence",
"of",
"the",
"we",
"is"
],
[
"and",
"variational",
"in",
"we",
"of",
"latent",
"to",
"uncertainty",
"the",
"posterior"
],
[
"the",
"we",
"inference",
"on",
"bayesian",
"for",
"and",
"of",
"to",
"in"
]
] | 13.015473 | all-MiniLM-L6-v2 | 0.357895 | -0.008285 | 0.179466 | 0.561053 |
ArXiv ML Papers | BERTopic | 46 | 40 | [
[
"learning",
"policy",
"in",
"of",
"the",
"we",
"and",
"to",
"that",
"reinforcement"
],
[
"learning",
"federated",
"the",
"to",
"data",
"devices",
"server",
"and",
"distributed",
"communication"
],
[
"that",
"the",
"and",
"of",
"we",
"to",
"fairness",
"fair",
"machine",
"learning"
],
[
"adversarial",
"attacks",
"attack",
"the",
"of",
"we",
"to",
"and",
"in",
"training"
],
[
"attacks",
"differentially",
"sensitive",
"that",
"private",
"privacy",
"the",
"to",
"differential",
"of"
],
[
"to",
"of",
"physics",
"neural",
"and",
"the",
"we",
"equations",
"in",
"data"
],
[
"to",
"signals",
"the",
"as",
"on",
"and",
"eeg",
"in",
"of",
"using"
],
[
"network",
"neural",
"relu",
"the",
"to",
"activation",
"in",
"of",
"networks",
"layer"
],
[
"protein",
"of",
"and",
"to",
"the",
"in",
"for",
"are",
"health",
"is"
],
[
"and",
"graph",
"of",
"we",
"the",
"graphs",
"to",
"node",
"network",
"in"
],
[
"the",
"user",
"of",
"to",
"item",
"recommender",
"we",
"items",
"recommendation",
"users"
],
[
"series",
"of",
"the",
"time",
"forecasting",
"and",
"to",
"in",
"for",
"model"
],
[
"speech",
"of",
"in",
"audio",
"to",
"and",
"the",
"speaker",
"we",
"for"
],
[
"of",
"models",
"language",
"we",
"to",
"the",
"and",
"on",
"in",
"attention"
],
[
"data",
"the",
"classification",
"label",
"is",
"in",
"of",
"learning",
"we",
"labels"
],
[
"image",
"for",
"of",
"to",
"medical",
"segmentation",
"the",
"and",
"in",
"with"
],
[
"is",
"of",
"the",
"and",
"for",
"to",
"in",
"variational",
"we",
"bayesian"
],
[
"convergence",
"is",
"optimization",
"the",
"stochastic",
"of",
"gradient",
"convex",
"and",
"we"
],
[
"generative",
"image",
"gan",
"images",
"of",
"to",
"gans",
"and",
"the",
"generator"
],
[
"metric",
"distance",
"the",
"to",
"we",
"data",
"of",
"learning",
"in",
"and"
],
[
"the",
"matrix",
"tensor",
"algorithm",
"rank",
"for",
"of",
"is",
"in",
"and"
],
[
"nas",
"pruning",
"to",
"on",
"of",
"and",
"neural",
"the",
"hardware",
"accuracy"
],
[
"the",
"point",
"scene",
"depth",
"and",
"3d",
"we",
"of",
"to",
"semantic"
],
[
"detection",
"object",
"the",
"on",
"and",
"to",
"of",
"in",
"cnn",
"for"
]
] | 31.63049 | all-MiniLM-L6-v2 | 0.425 | 0.002061 | 0.164638 | 0.597184 |
ArXiv ML Papers | BERTopic | 43 | 50 | [
[
"distributed",
"the",
"federated",
"learning",
"to",
"devices",
"data",
"server",
"communication",
"and"
],
[
"we",
"learning",
"of",
"in",
"the",
"to",
"policy",
"and",
"that",
"is"
],
[
"attacks",
"the",
"to",
"attack",
"adversarial",
"of",
"and",
"we",
"in",
"training"
],
[
"that",
"data",
"privacy",
"differentially",
"the",
"private",
"we",
"to",
"learning",
"differential"
],
[
"fairness",
"and",
"to",
"fair",
"the",
"of",
"machine",
"we",
"that",
"learning"
],
[
"to",
"the",
"and",
"of",
"speech",
"language",
"in",
"we",
"for",
"on"
],
[
"in",
"we",
"and",
"models",
"to",
"software",
"of",
"the",
"explanation",
"explanations"
],
[
"and",
"of",
"the",
"to",
"neural",
"we",
"physics",
"equations",
"in",
"data"
],
[
"network",
"the",
"neural",
"networks",
"relu",
"of",
"we",
"in",
"layer",
"to"
],
[
"to",
"on",
"eeg",
"signals",
"of",
"the",
"and",
"in",
"using",
"data"
],
[
"the",
"graphs",
"graph",
"of",
"and",
"nodes",
"node",
"to",
"we",
"in"
],
[
"side",
"protein",
"the",
"and",
"of",
"to",
"health",
"in",
"patient",
"is"
],
[
"time",
"the",
"of",
"and",
"forecasting",
"to",
"series",
"in",
"for",
"model"
],
[
"gradient",
"convex",
"and",
"of",
"the",
"we",
"stochastic",
"convergence",
"is",
"optimization"
],
[
"and",
"the",
"variational",
"of",
"to",
"we",
"in",
"for",
"is",
"bayesian"
],
[
"of",
"for",
"the",
"is",
"and",
"in",
"to",
"data",
"matrix",
"we"
],
[
"images",
"image",
"gans",
"gan",
"generative",
"to",
"the",
"and",
"of",
"we"
],
[
"of",
"and",
"the",
"pruning",
"to",
"neural",
"on",
"hardware",
"network",
"in"
],
[
"the",
"segmentation",
"of",
"and",
"to",
"in",
"image",
"medical",
"we",
"for"
],
[
"and",
"with",
"the",
"object",
"to",
"in",
"detection",
"of",
"on",
"we"
],
[
"we",
"and",
"depth",
"scene",
"the",
"3d",
"point",
"to",
"semantic",
"in"
]
] | 12.399162 | all-MiniLM-L6-v2 | 0.390476 | -0.006637 | 0.170969 | 0.587098 |
ArXiv ML Papers | BERTopic | 45 | 50 | [
[
"the",
"to",
"policy",
"and",
"learning",
"that",
"we",
"of",
"in",
"is"
],
[
"server",
"to",
"federated",
"data",
"the",
"learning",
"and",
"distributed",
"devices",
"training"
],
[
"we",
"and",
"to",
"the",
"that",
"attack",
"attacks",
"in",
"adversarial",
"of"
],
[
"fairness",
"fair",
"and",
"to",
"the",
"of",
"we",
"that",
"machine",
"learning"
],
[
"of",
"for",
"to",
"in",
"that",
"language",
"on",
"we",
"and",
"the"
],
[
"data",
"of",
"to",
"the",
"neural",
"equations",
"physics",
"we",
"and",
"in"
],
[
"the",
"neural",
"network",
"in",
"networks",
"we",
"that",
"of",
"relu",
"to"
],
[
"label",
"labels",
"is",
"of",
"the",
"to",
"classification",
"classifier",
"classifiers",
"in"
],
[
"time",
"of",
"forecasting",
"series",
"the",
"and",
"to",
"in",
"model",
"for"
],
[
"in",
"signals",
"and",
"the",
"eeg",
"of",
"using",
"on",
"to",
"as"
],
[
"are",
"the",
"of",
"health",
"and",
"to",
"protein",
"in",
"for",
"disease"
],
[
"graph",
"graphs",
"the",
"of",
"and",
"to",
"we",
"in",
"node",
"on"
],
[
"images",
"of",
"generative",
"gans",
"to",
"the",
"and",
"image",
"gan",
"generator"
],
[
"the",
"stochastic",
"convergence",
"gradient",
"convex",
"and",
"is",
"of",
"optimization",
"we"
],
[
"to",
"of",
"we",
"and",
"variational",
"the",
"in",
"uncertainty",
"is",
"for"
],
[
"of",
"to",
"and",
"hardware",
"the",
"pruning",
"neural",
"on",
"nas",
"network"
],
[
"in",
"of",
"to",
"the",
"image",
"segmentation",
"and",
"medical",
"for",
"with"
],
[
"the",
"of",
"in",
"and",
"to",
"pooling",
"with",
"object",
"on",
"image"
],
[
"3d",
"point",
"scene",
"depth",
"semantic",
"and",
"the",
"we",
"segmentation",
"driving"
],
[
"of",
"data",
"to",
"the",
"metric",
"kernel",
"we",
"in",
"distance",
"learning"
],
[
"the",
"tensor",
"in",
"for",
"algorithm",
"rank",
"of",
"is",
"matrix",
"and"
]
] | 498.173665 | all-MiniLM-L6-v2 | 0.404762 | -0.005565 | 0.17295 | 0.592389 |
ArXiv ML Papers | BERTopic | 46 | 50 | [
[
"in",
"policy",
"and",
"of",
"to",
"learning",
"the",
"we",
"that",
"is"
],
[
"distributed",
"learning",
"devices",
"server",
"federated",
"the",
"to",
"data",
"and",
"training"
],
[
"adversarial",
"the",
"attacks",
"to",
"attack",
"of",
"we",
"training",
"in",
"and"
],
[
"differentially",
"that",
"to",
"our",
"we",
"private",
"privacy",
"the",
"data",
"as"
],
[
"fairness",
"fair",
"and",
"to",
"the",
"of",
"machine",
"we",
"that",
"learning"
],
[
"to",
"and",
"the",
"in",
"of",
"we",
"on",
"that",
"language",
"for"
],
[
"data",
"we",
"and",
"the",
"to",
"in",
"physics",
"neural",
"of",
"equations"
],
[
"the",
"of",
"time",
"forecasting",
"and",
"series",
"to",
"in",
"model",
"for"
],
[
"relu",
"neural",
"networks",
"network",
"the",
"of",
"in",
"to",
"we",
"and"
],
[
"to",
"eeg",
"of",
"signals",
"the",
"and",
"on",
"in",
"data",
"using"
],
[
"the",
"graph",
"graphs",
"of",
"in",
"to",
"we",
"and",
"node",
"network"
],
[
"of",
"protein",
"the",
"and",
"health",
"to",
"in",
"for",
"from",
"is"
],
[
"of",
"classification",
"in",
"label",
"data",
"the",
"we",
"to",
"is",
"and"
],
[
"and",
"with",
"medical",
"in",
"to",
"of",
"images",
"the",
"segmentation",
"image"
],
[
"the",
"distance",
"metric",
"of",
"learning",
"in",
"we",
"and",
"to",
"data"
],
[
"the",
"algorithm",
"tensor",
"matrix",
"of",
"and",
"is",
"rank",
"for",
"in"
],
[
"generative",
"gan",
"image",
"gans",
"images",
"to",
"generator",
"and",
"of",
"the"
],
[
"nas",
"neural",
"on",
"network",
"of",
"to",
"pruning",
"hardware",
"and",
"the"
],
[
"and",
"of",
"detection",
"to",
"on",
"object",
"for",
"the",
"we",
"in"
],
[
"gradient",
"stochastic",
"is",
"we",
"the",
"convergence",
"of",
"optimization",
"and",
"convex"
],
[
"of",
"the",
"variational",
"we",
"and",
"is",
"in",
"bayesian",
"latent",
"to"
]
] | 529.846204 | all-MiniLM-L6-v2 | 0.37619 | -0.004715 | 0.17169 | 0.580724 |
ArXiv ML Papers | NMF | 43 | 10 | [
[
"the",
"and",
"of",
"proposed",
"to",
"on",
"by",
"based",
"method",
"is"
],
[
"to",
"the",
"be",
"this",
"neural",
"model",
"from",
"can",
"networks",
"an"
],
[
"to",
"an",
"of",
"this",
"the",
"number",
"these",
"are",
"our",
"with"
],
[
"models",
"both",
"are",
"with",
"based",
"on",
"and",
"methods",
"as",
"performance"
],
[
"show",
"as",
"the",
"we",
"on",
"with",
"our",
"propose",
"that",
"can"
],
[
"the",
"in",
"has",
"this",
"are",
"we",
"as",
"or",
"which",
"been"
],
[
"for",
"with",
"an",
"we",
"are",
"be",
"as",
"algorithm",
"can",
"used"
],
[
"training",
"the",
"for",
"is",
"data",
"from",
"as",
"on",
"model",
"models"
],
[
"machine",
"to",
"tasks",
"on",
"of",
"algorithms",
"and",
"learning",
"deep",
"reinforcement"
],
[
"algorithm",
"and",
"this",
"is",
"it",
"that",
"are",
"be",
"by",
"which"
]
] | 1.813817 | all-MiniLM-L6-v2 | 0.49 | -0.008151 | 0.195331 | 0.467242 |
ArXiv ML Papers | NMF | 44 | 10 | [
[
"method",
"on",
"of",
"to",
"is",
"proposed",
"and",
"based",
"the",
"by"
],
[
"the",
"from",
"this",
"to",
"model",
"be",
"neural",
"can",
"an",
"networks"
],
[
"to",
"an",
"of",
"this",
"the",
"number",
"these",
"are",
"our",
"with"
],
[
"models",
"both",
"are",
"with",
"based",
"on",
"and",
"methods",
"as",
"performance"
],
[
"show",
"as",
"the",
"we",
"on",
"with",
"our",
"propose",
"that",
"can"
],
[
"the",
"in",
"has",
"this",
"are",
"we",
"as",
"or",
"which",
"been"
],
[
"for",
"with",
"an",
"we",
"are",
"be",
"as",
"algorithm",
"can",
"used"
],
[
"training",
"the",
"for",
"is",
"data",
"from",
"as",
"on",
"model",
"models"
],
[
"machine",
"to",
"tasks",
"on",
"of",
"algorithms",
"and",
"learning",
"deep",
"reinforcement"
],
[
"algorithm",
"and",
"this",
"is",
"it",
"that",
"are",
"be",
"by",
"which"
]
] | 2.021709 | all-MiniLM-L6-v2 | 0.49 | -0.008151 | 0.195331 | 0.464778 |
ArXiv ML Papers | NMF | 45 | 10 | [
[
"the",
"and",
"of",
"proposed",
"to",
"on",
"by",
"based",
"method",
"is"
],
[
"the",
"from",
"this",
"to",
"model",
"be",
"neural",
"can",
"an",
"networks"
],
[
"to",
"an",
"of",
"this",
"the",
"number",
"these",
"are",
"our",
"with"
],
[
"models",
"both",
"are",
"with",
"based",
"on",
"and",
"methods",
"as",
"performance"
],
[
"show",
"as",
"the",
"we",
"on",
"with",
"our",
"propose",
"that",
"can"
],
[
"the",
"in",
"has",
"this",
"are",
"we",
"as",
"or",
"which",
"been"
],
[
"for",
"with",
"an",
"we",
"are",
"be",
"as",
"algorithm",
"can",
"used"
],
[
"training",
"the",
"for",
"is",
"data",
"from",
"as",
"on",
"model",
"models"
],
[
"machine",
"to",
"tasks",
"on",
"of",
"algorithms",
"and",
"learning",
"deep",
"reinforcement"
],
[
"algorithm",
"and",
"this",
"is",
"it",
"that",
"are",
"be",
"by",
"which"
]
] | 2.06287 | all-MiniLM-L6-v2 | 0.49 | -0.008151 | 0.195331 | 0.469553 |
ArXiv ML Papers | NMF | 46 | 10 | [
[
"the",
"and",
"of",
"proposed",
"to",
"on",
"by",
"based",
"method",
"is"
],
[
"to",
"the",
"be",
"this",
"neural",
"model",
"from",
"can",
"networks",
"an"
],
[
"to",
"an",
"of",
"this",
"the",
"number",
"these",
"are",
"our",
"with"
],
[
"models",
"both",
"are",
"with",
"based",
"on",
"and",
"methods",
"as",
"performance"
],
[
"show",
"as",
"the",
"we",
"on",
"with",
"our",
"propose",
"that",
"can"
],
[
"the",
"in",
"has",
"this",
"are",
"we",
"as",
"or",
"which",
"been"
],
[
"for",
"with",
"an",
"we",
"are",
"be",
"as",
"algorithm",
"can",
"used"
],
[
"training",
"the",
"for",
"is",
"data",
"from",
"as",
"on",
"model",
"models"
],
[
"machine",
"to",
"tasks",
"on",
"of",
"algorithms",
"and",
"learning",
"deep",
"reinforcement"
],
[
"algorithm",
"and",
"this",
"is",
"it",
"that",
"are",
"be",
"by",
"which"
]
] | 1.930904 | all-MiniLM-L6-v2 | 0.49 | -0.008151 | 0.195331 | 0.47231 |
ArXiv ML Papers | NMF | 43 | 20 | [
[
"proposed",
"of",
"the",
"to",
"by",
"between",
"information",
"results",
"using",
"two"
],
[
"from",
"the",
"it",
"due",
"an",
"to",
"their",
"framework",
"this",
"approach"
],
[
"these",
"set",
"the",
"this",
"number",
"an",
"of",
"new",
"state",
"art"
],
[
"both",
"and",
"two",
"models",
"are",
"the",
"research",
"ml",
"their",
"including"
],
[
"problem",
"which",
"our",
"show",
"we",
"and",
"the",
"propose",
"in",
"algorithm"
],
[
"in",
"or",
"are",
"work",
"paper",
"the",
"this",
"been",
"has",
"many"
],
[
"time",
"are",
"the",
"each",
"for",
"test",
"also",
"used",
"using",
"analysis"
],
[
"from",
"to",
"we",
"data",
"training",
"real",
"is",
"classification",
"of",
"time"
],
[
"deep",
"of",
"learning",
"machine",
"algorithms",
"reinforcement",
"tasks",
"policy",
"supervised",
"has"
],
[
"it",
"problem",
"which",
"where",
"algorithm",
"is",
"and",
"this",
"an",
"at"
],
[
"based",
"an",
"on",
"performance",
"by",
"datasets",
"this",
"tasks",
"classification",
"task"
],
[
"the",
"representations",
"are",
"this",
"show",
"not",
"more",
"than",
"also",
"that"
],
[
"training",
"deep",
"convolutional",
"to",
"is",
"network",
"networks",
"neural",
"we",
"layer"
],
[
"model",
"which",
"trained",
"we",
"performance",
"models",
"inference",
"in",
"our",
"based"
],
[
"to",
"have",
"an",
"are",
"as",
"such",
"or",
"well",
"these",
"using"
],
[
"with",
"dataset",
"high",
"accuracy",
"an",
"by",
"low",
"performance",
"results",
"while"
],
[
"and",
"state",
"methods",
"gradient",
"based",
"method",
"optimization",
"proposed",
"propose",
"our"
],
[
"we",
"training",
"models",
"robustness",
"attacks",
"adversarial",
"attack",
"to",
"against",
"the"
],
[
"by",
"be",
"this",
"can",
"from",
"it",
"or",
"which",
"used",
"how"
],
[
"the",
"graphs",
"graph",
"gnns",
"representation",
"nodes",
"node",
"and",
"our",
"we"
]
] | 2.422656 | all-MiniLM-L6-v2 | 0.565 | 0.013258 | 0.170953 | 0.561412 |
ArXiv ML Papers | NMF | 44 | 20 | [
[
"proposed",
"of",
"the",
"to",
"by",
"between",
"information",
"results",
"using",
"two"
],
[
"from",
"the",
"it",
"due",
"an",
"to",
"their",
"framework",
"this",
"approach"
],
[
"these",
"set",
"the",
"this",
"number",
"an",
"of",
"new",
"state",
"art"
],
[
"ml",
"the",
"research",
"both",
"are",
"and",
"two",
"models",
"their",
"including"
],
[
"problem",
"which",
"our",
"show",
"we",
"and",
"the",
"propose",
"in",
"algorithm"
],
[
"in",
"or",
"are",
"work",
"paper",
"the",
"this",
"been",
"has",
"many"
],
[
"time",
"are",
"the",
"each",
"for",
"test",
"also",
"used",
"using",
"analysis"
],
[
"from",
"to",
"we",
"data",
"training",
"real",
"is",
"classification",
"of",
"time"
],
[
"deep",
"of",
"learning",
"machine",
"algorithms",
"reinforcement",
"tasks",
"policy",
"supervised",
"has"
],
[
"it",
"problem",
"which",
"where",
"algorithm",
"is",
"and",
"this",
"an",
"at"
],
[
"based",
"an",
"on",
"performance",
"by",
"datasets",
"this",
"tasks",
"classification",
"task"
],
[
"that",
"show",
"are",
"this",
"not",
"representations",
"also",
"more",
"than",
"the"
],
[
"training",
"deep",
"convolutional",
"to",
"is",
"network",
"networks",
"neural",
"we",
"layer"
],
[
"model",
"which",
"trained",
"we",
"performance",
"models",
"inference",
"in",
"our",
"based"
],
[
"to",
"have",
"an",
"are",
"as",
"such",
"or",
"well",
"these",
"using"
],
[
"with",
"dataset",
"high",
"accuracy",
"an",
"by",
"low",
"performance",
"results",
"while"
],
[
"and",
"state",
"methods",
"gradient",
"based",
"method",
"optimization",
"proposed",
"propose",
"our"
],
[
"models",
"attack",
"attacks",
"training",
"to",
"we",
"the",
"robustness",
"adversarial",
"against"
],
[
"by",
"be",
"this",
"can",
"from",
"it",
"or",
"which",
"used",
"how"
],
[
"the",
"graphs",
"graph",
"gnns",
"representation",
"nodes",
"node",
"and",
"our",
"we"
]
] | 2.538431 | all-MiniLM-L6-v2 | 0.565 | 0.013258 | 0.170953 | 0.559352 |
ArXiv ML Papers | NMF | 45 | 20 | [
[
"by",
"to",
"of",
"the",
"proposed",
"results",
"using",
"between",
"information",
"two"
],
[
"from",
"the",
"it",
"due",
"an",
"to",
"their",
"framework",
"this",
"approach"
],
[
"these",
"set",
"the",
"this",
"number",
"an",
"of",
"new",
"state",
"art"
],
[
"ml",
"the",
"research",
"both",
"are",
"and",
"two",
"models",
"their",
"including"
],
[
"problem",
"which",
"our",
"show",
"we",
"and",
"the",
"propose",
"in",
"algorithm"
],
[
"in",
"or",
"are",
"work",
"paper",
"the",
"this",
"been",
"has",
"many"
],
[
"time",
"are",
"the",
"each",
"for",
"test",
"also",
"used",
"using",
"analysis"
],
[
"from",
"to",
"we",
"data",
"training",
"real",
"is",
"classification",
"of",
"time"
],
[
"deep",
"of",
"learning",
"machine",
"algorithms",
"reinforcement",
"tasks",
"policy",
"supervised",
"has"
],
[
"it",
"problem",
"which",
"where",
"algorithm",
"is",
"and",
"this",
"an",
"at"
],
[
"based",
"an",
"on",
"performance",
"by",
"datasets",
"this",
"tasks",
"classification",
"task"
],
[
"the",
"representations",
"are",
"this",
"show",
"not",
"more",
"than",
"also",
"that"
],
[
"training",
"deep",
"convolutional",
"to",
"is",
"network",
"networks",
"neural",
"we",
"layer"
],
[
"model",
"which",
"trained",
"we",
"performance",
"models",
"inference",
"in",
"our",
"based"
],
[
"to",
"have",
"an",
"are",
"as",
"such",
"or",
"well",
"these",
"using"
],
[
"with",
"dataset",
"high",
"accuracy",
"an",
"by",
"low",
"performance",
"results",
"while"
],
[
"and",
"state",
"methods",
"gradient",
"based",
"method",
"optimization",
"proposed",
"propose",
"our"
],
[
"we",
"training",
"models",
"robustness",
"attacks",
"adversarial",
"attack",
"to",
"against",
"the"
],
[
"by",
"be",
"this",
"can",
"from",
"it",
"or",
"which",
"used",
"how"
],
[
"the",
"graphs",
"graph",
"gnns",
"representation",
"nodes",
"node",
"and",
"our",
"we"
]
] | 2.535384 | all-MiniLM-L6-v2 | 0.565 | 0.013258 | 0.170953 | 0.559035 |
ArXiv ML Papers | NMF | 46 | 20 | [
[
"proposed",
"of",
"the",
"to",
"by",
"between",
"information",
"results",
"using",
"two"
],
[
"the",
"due",
"to",
"an",
"from",
"this",
"their",
"it",
"approach",
"framework"
],
[
"these",
"set",
"the",
"this",
"number",
"an",
"of",
"new",
"state",
"art"
],
[
"both",
"and",
"two",
"models",
"are",
"the",
"research",
"ml",
"their",
"including"
],
[
"problem",
"which",
"our",
"show",
"we",
"and",
"the",
"propose",
"in",
"algorithm"
],
[
"in",
"or",
"are",
"work",
"paper",
"the",
"this",
"been",
"has",
"many"
],
[
"time",
"are",
"the",
"each",
"for",
"test",
"also",
"used",
"using",
"analysis"
],
[
"from",
"to",
"we",
"data",
"training",
"real",
"is",
"classification",
"of",
"time"
],
[
"deep",
"of",
"learning",
"machine",
"algorithms",
"reinforcement",
"tasks",
"policy",
"supervised",
"has"
],
[
"it",
"problem",
"which",
"where",
"algorithm",
"is",
"and",
"this",
"an",
"at"
],
[
"based",
"an",
"on",
"performance",
"by",
"datasets",
"this",
"tasks",
"classification",
"task"
],
[
"the",
"representations",
"are",
"this",
"show",
"not",
"more",
"than",
"also",
"that"
],
[
"training",
"deep",
"convolutional",
"to",
"is",
"network",
"networks",
"neural",
"we",
"layer"
],
[
"model",
"which",
"trained",
"we",
"performance",
"models",
"inference",
"in",
"our",
"based"
],
[
"to",
"have",
"an",
"are",
"as",
"such",
"or",
"well",
"these",
"using"
],
[
"with",
"dataset",
"high",
"accuracy",
"an",
"by",
"low",
"performance",
"results",
"while"
],
[
"and",
"state",
"methods",
"gradient",
"based",
"method",
"optimization",
"proposed",
"propose",
"our"
],
[
"models",
"attack",
"attacks",
"training",
"to",
"we",
"the",
"robustness",
"adversarial",
"against"
],
[
"by",
"be",
"this",
"can",
"from",
"it",
"or",
"which",
"used",
"how"
],
[
"the",
"graphs",
"graph",
"gnns",
"representation",
"nodes",
"node",
"and",
"our",
"we"
]
] | 2.505962 | all-MiniLM-L6-v2 | 0.565 | 0.013258 | 0.170953 | 0.557773 |
ArXiv ML Papers | NMF | 43 | 30 | [
[
"of",
"to",
"problem",
"results",
"the",
"between",
"proposed",
"information",
"two",
"space"
],
[
"due",
"the",
"their",
"to",
"use",
"have",
"human",
"it",
"end",
"search"
],
[
"the",
"of",
"number",
"set",
"analysis",
"large",
"these",
"terms",
"art",
"and"
],
[
"research",
"and",
"two",
"the",
"both",
"accuracy",
"ml",
"between",
"including",
"their"
],
[
"the",
"demonstrate",
"we",
"which",
"show",
"use",
"present",
"propose",
"in",
"where"
],
[
"been",
"in",
"or",
"applications",
"has",
"work",
"many",
"the",
"recent",
"have"
],
[
"for",
"the",
"time",
"test",
"also",
"used",
"using",
"each",
"regression",
"prediction"
],
[
"data",
"and",
"classification",
"real",
"training",
"time",
"to",
"synthetic",
"large",
"supervised"
],
[
"supervised",
"learning",
"deep",
"machine",
"of",
"has",
"tasks",
"reinforcement",
"metric",
"agent"
],
[
"it",
"is",
"which",
"where",
"one",
"than",
"not",
"at",
"when",
"information"
],
[
"performance",
"trained",
"classification",
"or",
"art",
"tasks",
"work",
"on",
"task",
"datasets"
],
[
"that",
"show",
"results",
"not",
"representations",
"the",
"than",
"find",
"more",
"also"
],
[
"convolutional",
"and",
"networks",
"network",
"deep",
"training",
"neural",
"to",
"layer",
"performance"
],
[
"the",
"model",
"performance",
"and",
"which",
"in",
"trained",
"proposed",
"inference",
"parameters"
],
[
"or",
"have",
"well",
"to",
"such",
"as",
"the",
"these",
"been",
"not"
],
[
"dataset",
"accuracy",
"low",
"high",
"results",
"while",
"performance",
"compared",
"with",
"achieve"
],
[
"by",
"feature",
"high",
"series",
"images",
"approach",
"between",
"time",
"the",
"into"
],
[
"training",
"to",
"we",
"adversarial",
"attacks",
"the",
"robustness",
"against",
"examples",
"attack"
],
[
"this",
"paper",
"work",
"process",
"new",
"distribution",
"approach",
"problem",
"been",
"system"
],
[
"image",
"which",
"input",
"an",
"approach",
"features",
"classification",
"using",
"high",
"its"
],
[
"detection",
"the",
"based",
"on",
"system",
"feature",
"recognition",
"propose",
"proposed",
"have"
],
[
"image",
"which",
"used",
"or",
"quantum",
"how",
"it",
"be",
"they",
"can"
],
[
"more",
"different",
"they",
"these",
"are",
"their",
"algorithms",
"some",
"when",
"where"
],
[
"graph",
"node",
"nodes",
"and",
"the",
"graphs",
"representation",
"we",
"gnns",
"which"
],
[
"method",
"methods",
"proposed",
"deep",
"prediction",
"datasets",
"classification",
"existing",
"feature",
"large"
],
[
"of",
"models",
"generative",
"language",
"trained",
"these",
"fidelity",
"performance",
"inference",
"have"
],
[
"stochastic",
"function",
"policy",
"convex",
"and",
"convergence",
"gradient",
"problems",
"optimization",
"optimal"
],
[
"approach",
"results",
"our",
"we",
"method",
"framework",
"state",
"to",
"at",
"art"
],
[
"to",
"algorithm",
"problem",
"algorithms",
"matrix",
"proposed",
"linear",
"clustering",
"sample",
"rank"
],
[
"task",
"language",
"using",
"tasks",
"from",
"different",
"representations",
"knowledge",
"object",
"information"
]
] | 3.53165 | all-MiniLM-L6-v2 | 0.566667 | 0.022401 | 0.162399 | 0.62859 |
ArXiv ML Papers | NMF | 44 | 30 | [
[
"to",
"proposed",
"between",
"of",
"the",
"results",
"problem",
"two",
"information",
"space"
],
[
"human",
"to",
"their",
"due",
"use",
"it",
"end",
"have",
"framework",
"search"
],
[
"these",
"set",
"and",
"the",
"of",
"terms",
"analysis",
"number",
"art",
"large"
],
[
"two",
"both",
"the",
"and",
"research",
"accuracy",
"ml",
"including",
"between",
"their"
],
[
"show",
"use",
"which",
"we",
"in",
"present",
"the",
"propose",
"and",
"demonstrate"
],
[
"or",
"in",
"work",
"has",
"been",
"applications",
"many",
"recent",
"have",
"high"
],
[
"using",
"the",
"prediction",
"time",
"test",
"used",
"each",
"regression",
"for",
"also"
],
[
"data",
"and",
"classification",
"real",
"training",
"time",
"to",
"synthetic",
"large",
"supervised"
],
[
"supervised",
"of",
"deep",
"reinforcement",
"learning",
"machine",
"tasks",
"has",
"metric",
"agent"
],
[
"it",
"which",
"where",
"one",
"is",
"not",
"at",
"than",
"when",
"information"
],
[
"on",
"performance",
"tasks",
"datasets",
"task",
"or",
"trained",
"classification",
"work",
"art"
],
[
"the",
"find",
"representations",
"that",
"not",
"show",
"results",
"than",
"more",
"also"
],
[
"training",
"convolutional",
"networks",
"network",
"neural",
"and",
"deep",
"to",
"layer",
"performance"
],
[
"the",
"model",
"in",
"performance",
"and",
"which",
"trained",
"proposed",
"inference",
"parameters"
],
[
"well",
"such",
"as",
"to",
"have",
"or",
"been",
"these",
"not",
"using"
],
[
"dataset",
"high",
"compared",
"performance",
"accuracy",
"low",
"results",
"with",
"while",
"achieve"
],
[
"which",
"input",
"image",
"approach",
"an",
"features",
"classification",
"using",
"high",
"its"
],
[
"attacks",
"to",
"adversarial",
"training",
"the",
"we",
"robustness",
"attack",
"against",
"examples"
],
[
"paper",
"this",
"problem",
"approach",
"the",
"distribution",
"process",
"new",
"work",
"been"
],
[
"generative",
"fidelity",
"models",
"these",
"of",
"trained",
"language",
"performance",
"inference",
"have"
],
[
"methods",
"method",
"datasets",
"proposed",
"deep",
"feature",
"existing",
"classification",
"prediction",
"large"
],
[
"be",
"can",
"used",
"which",
"how",
"or",
"it",
"quantum",
"they",
"image"
],
[
"the",
"more",
"are",
"these",
"algorithms",
"when",
"where",
"they",
"different",
"their"
],
[
"the",
"graphs",
"and",
"node",
"nodes",
"graph",
"representation",
"gnns",
"we",
"which"
],
[
"detection",
"on",
"proposed",
"based",
"the",
"feature",
"propose",
"system",
"have",
"recognition"
],
[
"by",
"the",
"time",
"feature",
"series",
"approach",
"images",
"high",
"between",
"into"
],
[
"gradient",
"and",
"stochastic",
"optimization",
"policy",
"function",
"convex",
"convergence",
"problems",
"optimal"
],
[
"framework",
"results",
"our",
"approach",
"method",
"we",
"state",
"at",
"art",
"first"
],
[
"linear",
"problem",
"matrix",
"algorithm",
"proposed",
"to",
"algorithms",
"sample",
"clustering",
"rank"
],
[
"from",
"task",
"using",
"tasks",
"language",
"representations",
"different",
"knowledge",
"information",
"object"
]
] | 3.492299 | all-MiniLM-L6-v2 | 0.566667 | 0.022383 | 0.15836 | 0.63278 |
ArXiv ML Papers | NMF | 45 | 30 | [
[
"two",
"of",
"problem",
"to",
"proposed",
"results",
"between",
"the",
"information",
"using"
],
[
"to",
"the",
"due",
"use",
"their",
"it",
"human",
"have",
"search",
"framework"
],
[
"large",
"art",
"set",
"terms",
"state",
"analysis",
"of",
"number",
"new",
"these"
],
[
"the",
"and",
"both",
"two",
"ml",
"research",
"accuracy",
"including",
"their",
"study"
],
[
"and",
"present",
"which",
"we",
"the",
"use",
"show",
"propose",
"where",
"consider"
],
[
"or",
"in",
"been",
"work",
"has",
"many",
"the",
"applications",
"have",
"recent"
],
[
"also",
"time",
"for",
"used",
"each",
"prediction",
"test",
"one",
"using",
"the"
],
[
"training",
"data",
"real",
"time",
"classification",
"synthetic",
"large",
"to",
"supervised",
"from"
],
[
"machine",
"reinforcement",
"of",
"learning",
"tasks",
"deep",
"supervised",
"has",
"policy",
"metric"
],
[
"where",
"which",
"one",
"it",
"is",
"not",
"information",
"at",
"when",
"than"
],
[
"on",
"performance",
"datasets",
"tasks",
"or",
"task",
"work",
"focus",
"classification",
"trained"
],
[
"than",
"not",
"find",
"representations",
"show",
"that",
"the",
"results",
"also",
"more"
],
[
"to",
"neural",
"networks",
"deep",
"network",
"convolutional",
"training",
"layer",
"and",
"architecture"
],
[
"in",
"performance",
"parameters",
"model",
"which",
"and",
"inference",
"the",
"trained",
"proposed"
],
[
"well",
"such",
"to",
"been",
"as",
"have",
"these",
"or",
"the",
"not"
],
[
"performance",
"with",
"accuracy",
"high",
"compared",
"results",
"low",
"dataset",
"while",
"achieve"
],
[
"methods",
"method",
"proposed",
"optimization",
"existing",
"gradient",
"propose",
"and",
"datasets",
"problems"
],
[
"we",
"attacks",
"training",
"to",
"the",
"adversarial",
"robustness",
"against",
"attack",
"examples"
],
[
"process",
"been",
"new",
"work",
"problem",
"paper",
"this",
"approach",
"distribution",
"system"
],
[
"task",
"information",
"language",
"from",
"representations",
"different",
"features",
"using",
"knowledge",
"tasks"
],
[
"detection",
"based",
"proposed",
"the",
"on",
"feature",
"have",
"propose",
"system",
"information"
],
[
"it",
"be",
"or",
"can",
"how",
"quantum",
"which",
"used",
"they",
"systems"
],
[
"time",
"the",
"by",
"approach",
"series",
"high",
"between",
"feature",
"while",
"into"
],
[
"graph",
"the",
"which",
"nodes",
"and",
"gnns",
"graphs",
"node",
"we",
"representation"
],
[
"these",
"more",
"where",
"when",
"some",
"algorithms",
"their",
"different",
"are",
"they"
],
[
"inference",
"trained",
"fidelity",
"models",
"language",
"machine",
"these",
"of",
"generative",
"performance"
],
[
"an",
"network",
"input",
"features",
"which",
"its",
"and",
"using",
"approach",
"method"
],
[
"results",
"state",
"our",
"approach",
"framework",
"method",
"to",
"we",
"at",
"art"
],
[
"gradient",
"problem",
"algorithms",
"algorithm",
"optimization",
"convergence",
"to",
"problems",
"which",
"stochastic"
],
[
"text",
"images",
"image",
"gan",
"segmentation",
"classification",
"deep",
"which",
"we",
"performance"
]
] | 3.537155 | all-MiniLM-L6-v2 | 0.543333 | 0.020288 | 0.16415 | 0.626309 |
ArXiv ML Papers | NMF | 46 | 30 | [
[
"proposed",
"the",
"to",
"results",
"of",
"between",
"by",
"two",
"problem",
"information"
],
[
"the",
"it",
"due",
"search",
"to",
"human",
"use",
"this",
"end",
"their"
],
[
"these",
"art",
"set",
"analysis",
"of",
"terms",
"the",
"number",
"large",
"new"
],
[
"and",
"both",
"accuracy",
"the",
"ml",
"two",
"between",
"research",
"including",
"their"
],
[
"problem",
"which",
"the",
"show",
"we",
"propose",
"demonstrate",
"present",
"use",
"and"
],
[
"or",
"been",
"in",
"the",
"has",
"work",
"this",
"paper",
"applications",
"many"
],
[
"time",
"test",
"also",
"using",
"regression",
"used",
"the",
"for",
"each",
"one"
],
[
"supervised",
"and",
"large",
"training",
"data",
"real",
"labeled",
"synthetic",
"classification",
"time"
],
[
"reinforcement",
"deep",
"machine",
"tasks",
"learning",
"of",
"supervised",
"multi",
"has",
"metric"
],
[
"this",
"which",
"it",
"is",
"where",
"not",
"one",
"at",
"than",
"when"
],
[
"datasets",
"performance",
"tasks",
"on",
"classification",
"task",
"work",
"or",
"trained",
"art"
],
[
"not",
"this",
"show",
"than",
"that",
"representations",
"the",
"results",
"find",
"more"
],
[
"neural",
"deep",
"network",
"networks",
"convolutional",
"to",
"and",
"training",
"performance",
"layer"
],
[
"performance",
"the",
"which",
"and",
"model",
"trained",
"in",
"inference",
"parameters",
"speech"
],
[
"such",
"have",
"or",
"as",
"to",
"well",
"these",
"been",
"the",
"not"
],
[
"compared",
"low",
"results",
"accuracy",
"high",
"with",
"dataset",
"performance",
"while",
"achieve"
],
[
"method",
"prediction",
"propose",
"proposed",
"deep",
"feature",
"and",
"using",
"classification",
"optimization"
],
[
"adversarial",
"training",
"attacks",
"to",
"the",
"we",
"robustness",
"against",
"attack",
"examples"
],
[
"time",
"by",
"images",
"paper",
"this",
"system",
"approach",
"at",
"while",
"into"
],
[
"using",
"tasks",
"knowledge",
"language",
"representations",
"object",
"task",
"from",
"different",
"information"
],
[
"on",
"detection",
"the",
"have",
"feature",
"recognition",
"system",
"propose",
"proposed",
"based"
],
[
"can",
"which",
"be",
"used",
"or",
"quantum",
"it",
"how",
"they",
"image"
],
[
"algorithms",
"are",
"these",
"they",
"when",
"where",
"different",
"the",
"more",
"their"
],
[
"graphs",
"and",
"node",
"nodes",
"the",
"representation",
"gnns",
"graph",
"we",
"structure"
],
[
"have",
"methods",
"datasets",
"which",
"performance",
"and",
"been",
"both",
"existing",
"has"
],
[
"fidelity",
"of",
"performance",
"generative",
"prediction",
"language",
"these",
"models",
"trained",
"have"
],
[
"policy",
"and",
"function",
"convex",
"optimization",
"stochastic",
"gradient",
"problems",
"convergence",
"optimal"
],
[
"results",
"to",
"framework",
"in",
"art",
"our",
"approach",
"at",
"we",
"state"
],
[
"problem",
"algorithm",
"algorithms",
"to",
"matrix",
"proposed",
"clustering",
"linear",
"sample",
"rank"
],
[
"which",
"image",
"input",
"features",
"an",
"approach",
"high",
"its",
"using",
"classification"
]
] | 3.519023 | all-MiniLM-L6-v2 | 0.556667 | 0.019611 | 0.163618 | 0.617932 |
ArXiv ML Papers | NMF | 43 | 40 | [
[
"results",
"of",
"strategy",
"between",
"proposed",
"information",
"the",
"test",
"was",
"error"
],
[
"due",
"their",
"approach",
"framework",
"use",
"to",
"compared",
"human",
"able",
"often"
],
[
"analysis",
"number",
"of",
"set",
"and",
"these",
"new",
"large",
"art",
"terms"
],
[
"including",
"and",
"both",
"two",
"accuracy",
"between",
"ml",
"research",
"study",
"respectively"
],
[
"demonstrate",
"we",
"present",
"new",
"the",
"propose",
"use",
"show",
"study",
"these"
],
[
"or",
"work",
"in",
"particular",
"addition",
"space",
"order",
"terms",
"context",
"many"
],
[
"test",
"analysis",
"used",
"one",
"using",
"for",
"also",
"the",
"regression",
"each"
],
[
"driven",
"data",
"and",
"synthetic",
"classification",
"large",
"real",
"high",
"techniques",
"labeled"
],
[
"learning",
"machine",
"of",
"agent",
"metric",
"reinforcement",
"supervised",
"policy",
"learn",
"the"
],
[
"when",
"where",
"one",
"the",
"at",
"than",
"it",
"is",
"its",
"information"
],
[
"previous",
"work",
"or",
"datasets",
"on",
"focus",
"real",
"rely",
"all",
"dataset"
],
[
"show",
"than",
"results",
"also",
"find",
"not",
"more",
"the",
"that",
"representations"
],
[
"and",
"networks",
"neural",
"convolutional",
"over",
"architectures",
"have",
"cnns",
"physics",
"weights"
],
[
"uncertainty",
"inference",
"predictions",
"parameters",
"trained",
"privacy",
"the",
"model",
"proposed",
"to"
],
[
"such",
"to",
"the",
"as",
"well",
"or",
"known",
"through",
"using",
"processing"
],
[
"with",
"accuracy",
"results",
"all",
"dataset",
"high",
"while",
"compared",
"the",
"respect"
],
[
"between",
"high",
"by",
"into",
"approach",
"the",
"filter",
"while",
"function",
"class"
],
[
"of",
"adversarial",
"to",
"robustness",
"against",
"attack",
"attacks",
"the",
"detection",
"and"
],
[
"approach",
"work",
"new",
"paper",
"this",
"process",
"system",
"challenge",
"presents",
"video"
],
[
"language",
"source",
"domain",
"tasks",
"target",
"multi",
"knowledge",
"task",
"different",
"representations"
],
[
"and",
"samples",
"distribution",
"problems",
"problem",
"optimal",
"the",
"matrix",
"sample",
"rank"
],
[
"be",
"used",
"how",
"it",
"can",
"or",
"systems",
"quantum",
"they",
"at"
],
[
"these",
"they",
"algorithms",
"where",
"when",
"are",
"the",
"more",
"some",
"different"
],
[
"node",
"graph",
"graphs",
"and",
"the",
"nodes",
"representation",
"gnns",
"we",
"clustering"
],
[
"datasets",
"proposed",
"prediction",
"methods",
"method",
"propose",
"large",
"existing",
"noise",
"optimization"
],
[
"prediction",
"generative",
"inference",
"trained",
"fidelity",
"models",
"language",
"of",
"machine",
"these"
],
[
"gradient",
"stochastic",
"optimization",
"policy",
"convergence",
"and",
"convex",
"function",
"problems",
"descent"
],
[
"results",
"framework",
"approach",
"method",
"our",
"we",
"first",
"state",
"work",
"best"
],
[
"algorithm",
"proposed",
"clustering",
"to",
"algorithms",
"the",
"label",
"regret",
"efficient",
"multi"
],
[
"features",
"using",
"from",
"set",
"information",
"representations",
"language",
"between",
"object",
"different"
],
[
"detection",
"based",
"proposed",
"information",
"the",
"propose",
"system",
"systems",
"user",
"state"
],
[
"time",
"of",
"real",
"series",
"at",
"forecasting",
"temporal",
"world",
"state",
"hierarchical"
],
[
"which",
"of",
"not",
"propose",
"two",
"feature",
"information",
"only",
"to",
"state"
],
[
"state",
"performance",
"classification",
"of",
"art",
"accuracy",
"better",
"achieve",
"high",
"using"
],
[
"images",
"image",
"gan",
"segmentation",
"text",
"generative",
"resolution",
"object",
"we",
"visual"
],
[
"neural",
"layer",
"network",
"to",
"structure",
"prediction",
"show",
"nodes",
"architecture",
"convolutional"
],
[
"deep",
"feature",
"speech",
"learning",
"features",
"to",
"detection",
"accuracy",
"results",
"ensemble"
],
[
"an",
"features",
"input",
"approach",
"using",
"its",
"system",
"technique",
"high",
"it"
],
[
"train",
"during",
"set",
"loss",
"training",
"pre",
"trained",
"to",
"we",
"federated"
],
[
"has",
"have",
"it",
"been",
"not",
"these",
"machine",
"the",
"methods",
"research"
]
] | 5.106921 | all-MiniLM-L6-v2 | 0.5825 | 0.008913 | 0.149585 | 0.670219 |
ArXiv ML Papers | NMF | 44 | 40 | [
[
"the",
"error",
"of",
"was",
"proposed",
"between",
"results",
"information",
"two",
"test"
],
[
"due",
"it",
"to",
"human",
"search",
"use",
"their",
"have",
"approach",
"able"
],
[
"large",
"these",
"number",
"set",
"of",
"terms",
"art",
"analysis",
"and",
"new"
],
[
"both",
"and",
"research",
"between",
"ml",
"study",
"accuracy",
"their",
"including",
"two"
],
[
"provide",
"use",
"show",
"we",
"demonstrate",
"propose",
"present",
"new",
"consider",
"study"
],
[
"many",
"high",
"recent",
"have",
"has",
"applications",
"in",
"work",
"or",
"been"
],
[
"used",
"for",
"test",
"regression",
"the",
"analysis",
"also",
"one",
"using",
"large"
],
[
"real",
"the",
"techniques",
"large",
"high",
"driven",
"synthetic",
"and",
"classification",
"data"
],
[
"learning",
"machine",
"active",
"framework",
"has",
"supervised",
"metric",
"learn",
"of",
"algorithms"
],
[
"is",
"it",
"when",
"where",
"the",
"at",
"one",
"than",
"its",
"information"
],
[
"the",
"work",
"datasets",
"or",
"real",
"on",
"performance",
"previous",
"focus",
"rely"
],
[
"show",
"that",
"not",
"results",
"than",
"also",
"the",
"more",
"representations",
"find"
],
[
"neural",
"network",
"convolutional",
"networks",
"and",
"layer",
"architecture",
"performance",
"architectures",
"layers"
],
[
"model",
"the",
"performance",
"inference",
"and",
"trained",
"uncertainty",
"predictions",
"parameters",
"in"
],
[
"to",
"well",
"the",
"such",
"as",
"have",
"using",
"been",
"these",
"or"
],
[
"with",
"accuracy",
"performance",
"compared",
"the",
"results",
"dataset",
"high",
"low",
"while"
],
[
"the",
"attacks",
"attack",
"to",
"adversarial",
"of",
"robustness",
"detection",
"against",
"and"
],
[
"training",
"performance",
"trained",
"in",
"to",
"during",
"and",
"we",
"loss",
"pre"
],
[
"paper",
"approach",
"problem",
"been",
"this",
"new",
"process",
"work",
"the",
"challenge"
],
[
"algorithms",
"these",
"where",
"are",
"more",
"the",
"when",
"they",
"their",
"some"
],
[
"system",
"based",
"the",
"propose",
"proposed",
"systems",
"detection",
"have",
"recognition",
"user"
],
[
"how",
"be",
"can",
"used",
"it",
"or",
"quantum",
"they",
"to",
"systems"
],
[
"between",
"filter",
"by",
"while",
"the",
"dimensional",
"approach",
"high",
"into",
"images"
],
[
"graph",
"node",
"nodes",
"and",
"gnns",
"graphs",
"the",
"representation",
"we",
"structure"
],
[
"performance",
"datasets",
"methods",
"have",
"the",
"both",
"existing",
"and",
"many",
"other"
],
[
"fidelity",
"models",
"generative",
"machine",
"these",
"trained",
"inference",
"language",
"prediction",
"high"
],
[
"stochastic",
"gradient",
"and",
"optimization",
"problems",
"function",
"convergence",
"order",
"descent",
"convex"
],
[
"framework",
"our",
"approach",
"results",
"we",
"best",
"state",
"art",
"first",
"experiments"
],
[
"an",
"the",
"it",
"input",
"approach",
"high",
"technique",
"system",
"its",
"using"
],
[
"from",
"using",
"different",
"object",
"set",
"language",
"information",
"representations",
"samples",
"between"
],
[
"language",
"knowledge",
"multi",
"tasks",
"task",
"prediction",
"performance",
"different",
"each",
"meta"
],
[
"series",
"time",
"of",
"in",
"at",
"real",
"forecasting",
"temporal",
"world",
"state"
],
[
"two",
"which",
"to",
"propose",
"of",
"not",
"only",
"has",
"distribution",
"local"
],
[
"to",
"problem",
"algorithms",
"matrix",
"the",
"algorithm",
"proposed",
"sample",
"clustering",
"linear"
],
[
"segmentation",
"images",
"image",
"text",
"gan",
"generative",
"we",
"to",
"resolution",
"object"
],
[
"domains",
"domain",
"knowledge",
"transfer",
"source",
"the",
"target",
"to",
"adaptation",
"proposed"
],
[
"deep",
"have",
"been",
"learning",
"has",
"ensemble",
"accuracy",
"recognition",
"paper",
"results"
],
[
"prediction",
"method",
"proposed",
"propose",
"and",
"noise",
"optimization",
"large",
"using",
"low"
],
[
"agents",
"policy",
"rl",
"reward",
"reinforcement",
"state",
"the",
"and",
"agent",
"learning"
],
[
"and",
"features",
"feature",
"to",
"classification",
"classifier",
"attention",
"in",
"the",
"selection"
]
] | 5.076314 | all-MiniLM-L6-v2 | 0.56 | 0.017912 | 0.157493 | 0.644569 |
ArXiv ML Papers | NMF | 45 | 40 | [
[
"the",
"of",
"proposed",
"results",
"between",
"error",
"two",
"information",
"test",
"problem"
],
[
"use",
"their",
"to",
"due",
"it",
"search",
"framework",
"human",
"have",
"however"
],
[
"and",
"of",
"number",
"these",
"set",
"art",
"terms",
"large",
"analysis",
"study"
],
[
"both",
"and",
"two",
"ml",
"research",
"accuracy",
"study",
"including",
"their",
"between"
],
[
"we",
"propose",
"demonstrate",
"present",
"use",
"show",
"new",
"provide",
"consider",
"study"
],
[
"has",
"many",
"applications",
"in",
"or",
"been",
"work",
"recent",
"high",
"particular"
],
[
"test",
"analysis",
"for",
"also",
"used",
"regression",
"large",
"one",
"using",
"set"
],
[
"synthetic",
"large",
"classification",
"driven",
"data",
"training",
"real",
"high",
"labeled",
"techniques"
],
[
"the",
"metric",
"learning",
"of",
"supervised",
"machine",
"framework",
"active",
"learn",
"to"
],
[
"it",
"the",
"is",
"one",
"when",
"where",
"than",
"at",
"and",
"its"
],
[
"performance",
"datasets",
"on",
"work",
"or",
"focus",
"previous",
"real",
"rely",
"all"
],
[
"show",
"that",
"not",
"the",
"results",
"more",
"than",
"also",
"find",
"representations"
],
[
"neural",
"architectures",
"convolutional",
"more",
"have",
"training",
"over",
"cnns",
"and",
"networks"
],
[
"trained",
"parameters",
"model",
"inference",
"performance",
"the",
"and",
"to",
"uncertainty",
"predictions"
],
[
"well",
"or",
"not",
"as",
"have",
"the",
"to",
"these",
"such",
"been"
],
[
"compared",
"accuracy",
"results",
"with",
"performance",
"high",
"dataset",
"while",
"low",
"achieve"
],
[
"method",
"proposed",
"prediction",
"the",
"and",
"large",
"propose",
"noise",
"using",
"optimization"
],
[
"to",
"robustness",
"the",
"training",
"adversarial",
"attacks",
"we",
"attack",
"against",
"examples"
],
[
"paper",
"the",
"problem",
"distribution",
"this",
"process",
"new",
"approach",
"been",
"work"
],
[
"which",
"not",
"has",
"of",
"propose",
"two",
"the",
"only",
"distribution",
"information"
],
[
"its",
"approach",
"using",
"input",
"an",
"the",
"technique",
"high",
"system",
"it"
],
[
"or",
"how",
"it",
"used",
"they",
"quantum",
"be",
"can",
"systems",
"at"
],
[
"are",
"these",
"where",
"the",
"more",
"they",
"when",
"different",
"their",
"some"
],
[
"nodes",
"we",
"graph",
"and",
"representation",
"graphs",
"gnns",
"the",
"node",
"structure"
],
[
"training",
"different",
"knowledge",
"source",
"language",
"domain",
"performance",
"target",
"task",
"tasks"
],
[
"these",
"models",
"fidelity",
"machine",
"trained",
"generative",
"inference",
"performance",
"language",
"prediction"
],
[
"gradient",
"convergence",
"function",
"and",
"convex",
"stochastic",
"optimization",
"problems",
"descent",
"order"
],
[
"our",
"approach",
"results",
"first",
"framework",
"we",
"state",
"to",
"art",
"best"
],
[
"problem",
"the",
"to",
"matrix",
"sample",
"algorithm",
"regret",
"rank",
"clustering",
"linear"
],
[
"set",
"object",
"samples",
"language",
"using",
"information",
"different",
"from",
"between",
"representations"
],
[
"system",
"based",
"have",
"detection",
"proposed",
"propose",
"systems",
"the",
"recognition",
"user"
],
[
"series",
"in",
"time",
"real",
"at",
"of",
"forecasting",
"temporal",
"state",
"world"
],
[
"other",
"datasets",
"the",
"existing",
"have",
"both",
"performance",
"methods",
"many",
"been"
],
[
"algorithms",
"to",
"two",
"the",
"problems",
"other",
"search",
"these",
"variance",
"more"
],
[
"between",
"while",
"the",
"high",
"dimensional",
"images",
"by",
"approach",
"into",
"filter"
],
[
"neural",
"to",
"performance",
"layer",
"network",
"training",
"structure",
"architecture",
"convolutional",
"prediction"
],
[
"been",
"deep",
"learning",
"have",
"ensemble",
"accuracy",
"has",
"recognition",
"paper",
"speech"
],
[
"segmentation",
"resolution",
"images",
"image",
"object",
"gan",
"generative",
"we",
"text",
"trained"
],
[
"the",
"learning",
"policy",
"agent",
"and",
"reward",
"state",
"agents",
"reinforcement",
"rl"
],
[
"selection",
"in",
"features",
"attention",
"to",
"the",
"feature",
"classification",
"classifier",
"and"
]
] | 5.337978 | all-MiniLM-L6-v2 | 0.5525 | 0.016879 | 0.153486 | 0.64701 |
ArXiv ML Papers | NMF | 46 | 40 | [
[
"the",
"problem",
"error",
"results",
"of",
"between",
"proposed",
"two",
"space",
"information"
],
[
"use",
"due",
"to",
"it",
"human",
"their",
"have",
"framework",
"search",
"approach"
],
[
"terms",
"analysis",
"set",
"number",
"of",
"these",
"new",
"and",
"large",
"art"
],
[
"research",
"two",
"both",
"and",
"ml",
"including",
"between",
"accuracy",
"their",
"study"
],
[
"new",
"propose",
"we",
"demonstrate",
"study",
"provide",
"consider",
"show",
"present",
"use"
],
[
"has",
"or",
"the",
"been",
"recent",
"many",
"work",
"applications",
"have",
"in"
],
[
"one",
"using",
"used",
"also",
"the",
"test",
"for",
"analysis",
"regression",
"large"
],
[
"large",
"driven",
"real",
"data",
"classification",
"synthetic",
"high",
"and",
"techniques",
"labeled"
],
[
"the",
"of",
"supervised",
"reinforcement",
"machine",
"learning",
"metric",
"policy",
"learn",
"agent"
],
[
"when",
"is",
"one",
"where",
"it",
"than",
"the",
"at",
"its",
"and"
],
[
"on",
"datasets",
"the",
"focus",
"real",
"work",
"or",
"rely",
"previous",
"all"
],
[
"not",
"show",
"more",
"the",
"results",
"also",
"that",
"than",
"find",
"representations"
],
[
"network",
"structure",
"layer",
"architecture",
"nodes",
"neural",
"to",
"and",
"prediction",
"convolutional"
],
[
"the",
"model",
"trained",
"and",
"in",
"inference",
"uncertainty",
"parameters",
"to",
"predictions"
],
[
"well",
"as",
"such",
"to",
"or",
"the",
"have",
"these",
"using",
"been"
],
[
"with",
"low",
"dataset",
"accuracy",
"results",
"high",
"compared",
"the",
"respect",
"all"
],
[
"an",
"approach",
"the",
"input",
"its",
"using",
"system",
"it",
"high",
"technique"
],
[
"to",
"attacks",
"the",
"against",
"of",
"attack",
"adversarial",
"robustness",
"detection",
"and"
],
[
"approach",
"paper",
"distribution",
"new",
"the",
"problem",
"work",
"process",
"this",
"been"
],
[
"state",
"the",
"distribution",
"which",
"not",
"of",
"only",
"propose",
"two",
"has"
],
[
"the",
"based",
"detection",
"proposed",
"system",
"propose",
"approaches",
"user",
"systems",
"have"
],
[
"they",
"can",
"quantum",
"or",
"be",
"used",
"it",
"how",
"to",
"systems"
],
[
"these",
"more",
"are",
"some",
"algorithms",
"where",
"the",
"their",
"when",
"they"
],
[
"representation",
"graphs",
"graph",
"and",
"nodes",
"node",
"the",
"gnns",
"we",
"clustering"
],
[
"by",
"the",
"approach",
"between",
"high",
"while",
"filter",
"dimensional",
"function",
"into"
],
[
"models",
"machine",
"generative",
"trained",
"these",
"fidelity",
"language",
"of",
"prediction",
"inference"
],
[
"policy",
"stochastic",
"function",
"optimization",
"gradient",
"and",
"convergence",
"problems",
"convex",
"optimal"
],
[
"approach",
"results",
"framework",
"our",
"we",
"experiments",
"art",
"state",
"to",
"first"
],
[
"algorithms",
"algorithm",
"proposed",
"to",
"clustering",
"matrix",
"the",
"problem",
"linear",
"sample"
],
[
"object",
"different",
"using",
"set",
"from",
"information",
"between",
"samples",
"language",
"representations"
],
[
"domain",
"tasks",
"task",
"knowledge",
"language",
"multi",
"different",
"to",
"target",
"source"
],
[
"series",
"time",
"real",
"in",
"world",
"of",
"at",
"temporal",
"forecasting",
"state"
],
[
"have",
"the",
"methods",
"datasets",
"problems",
"both",
"other",
"existing",
"been",
"many"
],
[
"performance",
"of",
"state",
"art",
"to",
"accuracy",
"high",
"proposed",
"classification",
"achieve"
],
[
"image",
"generative",
"segmentation",
"images",
"we",
"text",
"gan",
"resolution",
"trained",
"object"
],
[
"architectures",
"cnns",
"over",
"neural",
"and",
"networks",
"convolutional",
"physics",
"have",
"recurrent"
],
[
"deep",
"learning",
"to",
"been",
"has",
"accuracy",
"ensemble",
"have",
"paper",
"recognition"
],
[
"the",
"features",
"in",
"classification",
"feature",
"selection",
"to",
"attention",
"classifier",
"information"
],
[
"set",
"trained",
"training",
"in",
"pre",
"during",
"federated",
"we",
"to",
"loss"
],
[
"proposed",
"optimization",
"low",
"prediction",
"large",
"using",
"method",
"noise",
"propose",
"existing"
]
] | 5.098675 | all-MiniLM-L6-v2 | 0.5475 | 0.01484 | 0.159135 | 0.638216 |
ArXiv ML Papers | NMF | 43 | 50 | [
[
"of",
"test",
"proposed",
"error",
"the",
"was",
"between",
"results",
"strategy",
"one"
],
[
"to",
"due",
"use",
"human",
"able",
"approach",
"their",
"often",
"compared",
"framework"
],
[
"set",
"number",
"these",
"of",
"analysis",
"large",
"terms",
"study",
"parameters",
"new"
],
[
"accuracy",
"including",
"research",
"and",
"ml",
"two",
"both",
"study",
"between",
"respectively"
],
[
"we",
"use",
"propose",
"show",
"present",
"new",
"study",
"demonstrate",
"provide",
"these"
],
[
"context",
"in",
"addition",
"computational",
"terms",
"work",
"may",
"particular",
"many",
"or"
],
[
"test",
"one",
"used",
"regression",
"for",
"also",
"analysis",
"each",
"large",
"fast"
],
[
"data",
"real",
"framework",
"labeled",
"driven",
"high",
"large",
"classification",
"synthetic",
"techniques"
],
[
"learning",
"of",
"metric",
"active",
"machine",
"supervised",
"learn",
"framework",
"reinforcement",
"self"
],
[
"one",
"when",
"it",
"is",
"and",
"the",
"than",
"its",
"at",
"where"
],
[
"on",
"work",
"datasets",
"focus",
"or",
"rely",
"all",
"user",
"real",
"previous"
],
[
"these",
"where",
"more",
"they",
"when",
"are",
"their",
"some",
"new",
"or"
],
[
"network",
"structure",
"layer",
"neural",
"architecture",
"nodes",
"convolutional",
"show",
"prediction",
"learned"
],
[
"parameters",
"trained",
"uncertainty",
"inference",
"while",
"the",
"predictions",
"model",
"free",
"global"
],
[
"through",
"to",
"as",
"well",
"such",
"or",
"using",
"processing",
"known",
"continuous"
],
[
"with",
"accuracy",
"results",
"dataset",
"all",
"respect",
"compared",
"high",
"while",
"multiple"
],
[
"art",
"of",
"state",
"the",
"propose",
"datasets",
"classification",
"results",
"over",
"approaches"
],
[
"trained",
"loss",
"we",
"training",
"during",
"train",
"pre",
"set",
"federated",
"large"
],
[
"approach",
"paper",
"process",
"presents",
"new",
"challenge",
"work",
"this",
"system",
"class"
],
[
"user",
"the",
"between",
"information",
"semantic",
"channel",
"two",
"order",
"variables",
"label"
],
[
"has",
"have",
"it",
"been",
"not",
"these",
"machine",
"the",
"their",
"research"
],
[
"can",
"be",
"used",
"to",
"the",
"it",
"they",
"how",
"quantum",
"or"
],
[
"filter",
"between",
"into",
"approach",
"high",
"by",
"the",
"while",
"however",
"images"
],
[
"the",
"graph",
"graphs",
"nodes",
"gnns",
"node",
"representation",
"and",
"we",
"clustering"
],
[
"than",
"more",
"results",
"show",
"also",
"that",
"not",
"find",
"prior",
"representations"
],
[
"models",
"trained",
"fidelity",
"generative",
"inference",
"prediction",
"these",
"at",
"language",
"machine"
],
[
"system",
"framework",
"high",
"approach",
"input",
"technique",
"3d",
"the",
"its",
"an"
],
[
"our",
"results",
"first",
"framework",
"approach",
"best",
"work",
"at",
"experiments",
"only"
],
[
"to",
"the",
"algorithm",
"proposed",
"selection",
"clustering",
"sample",
"exchange",
"is",
"convergence"
],
[
"from",
"using",
"different",
"the",
"set",
"between",
"object",
"language",
"knowledge",
"representations"
],
[
"based",
"the",
"proposed",
"systems",
"user",
"on",
"flow",
"propose",
"approaches",
"is"
],
[
"forecasting",
"real",
"series",
"of",
"hierarchical",
"time",
"world",
"temporal",
"forecast",
"at"
],
[
"methods",
"datasets",
"both",
"many",
"existing",
"other",
"and",
"be",
"linear",
"hierarchical"
],
[
"task",
"knowledge",
"different",
"multi",
"prediction",
"tasks",
"solving",
"each",
"meta",
"language"
],
[
"images",
"image",
"gan",
"resolution",
"medical",
"generative",
"text",
"trained",
"segmentation",
"we"
],
[
"networks",
"neural",
"convolutional",
"over",
"architectures",
"physics",
"and",
"have",
"cnns",
"more"
],
[
"adversarial",
"attacks",
"to",
"attack",
"the",
"against",
"robustness",
"and",
"of",
"robust"
],
[
"proposed",
"the",
"method",
"prediction",
"has",
"propose",
"large",
"noise",
"using",
"new"
],
[
"control",
"policy",
"the",
"policies",
"learning",
"and",
"reinforcement",
"rl",
"reward",
"optimal"
],
[
"results",
"recognition",
"deep",
"paper",
"ensemble",
"learning",
"accuracy",
"in",
"to",
"dnn"
],
[
"optimization",
"stochastic",
"convergence",
"gradient",
"function",
"problems",
"convex",
"descent",
"and",
"order"
],
[
"better",
"achieve",
"their",
"computing",
"high",
"cost",
"to",
"performance",
"accuracy",
"design"
],
[
"domain",
"target",
"the",
"source",
"adaptation",
"knowledge",
"transfer",
"domains",
"to",
"datasets"
],
[
"dataset",
"using",
"object",
"detection",
"system",
"classification",
"the",
"rate",
"detect",
"attacks"
],
[
"of",
"two",
"not",
"which",
"to",
"the",
"propose",
"distribution",
"only",
"has"
],
[
"classification",
"feature",
"features",
"and",
"of",
"the",
"selection",
"to",
"results",
"attention"
],
[
"two",
"more",
"algorithms",
"problems",
"variance",
"search",
"other",
"than",
"the",
"robust"
],
[
"matrix",
"rank",
"problems",
"problem",
"low",
"optimal",
"samples",
"distribution",
"linear",
"given"
],
[
"agents",
"communication",
"agent",
"to",
"each",
"where",
"regret",
"the",
"at",
"multi"
],
[
"visual",
"and",
"language",
"speech",
"to",
"audio",
"the",
"text",
"end",
"attention"
]
] | 6.24702 | all-MiniLM-L6-v2 | 0.56 | 0.007192 | 0.147988 | 0.67742 |
ArXiv ML Papers | NMF | 44 | 50 | [
[
"between",
"information",
"the",
"error",
"proposed",
"results",
"was",
"of",
"strategy",
"conditions"
],
[
"compared",
"due",
"to",
"use",
"able",
"their",
"approach",
"framework",
"often",
"end"
],
[
"of",
"and",
"number",
"set",
"analysis",
"these",
"terms",
"art",
"large",
"study"
],
[
"various",
"respectively",
"research",
"including",
"two",
"between",
"study",
"both",
"and",
"covid"
],
[
"use",
"show",
"demonstrate",
"provide",
"present",
"study",
"new",
"we",
"propose",
"introduce"
],
[
"context",
"particular",
"work",
"or",
"in",
"addition",
"terms",
"order",
"many",
"computational"
],
[
"test",
"used",
"one",
"for",
"analysis",
"also",
"large",
"regression",
"fast",
"useful"
],
[
"data",
"real",
"large",
"driven",
"synthetic",
"labeled",
"techniques",
"process",
"sets",
"framework"
],
[
"learning",
"supervised",
"metric",
"of",
"machine",
"framework",
"active",
"learn",
"self",
"has"
],
[
"show",
"that",
"not",
"than",
"also",
"more",
"find",
"results",
"representations",
"such"
],
[
"datasets",
"on",
"work",
"or",
"focus",
"user",
"all",
"previous",
"rely",
"trained"
],
[
"and",
"prediction",
"accuracy",
"is",
"of",
"dataset",
"traffic",
"two",
"speed",
"market"
],
[
"structure",
"show",
"layer",
"architecture",
"neural",
"network",
"convolutional",
"learned",
"nodes",
"teacher"
],
[
"trained",
"privacy",
"parameters",
"inference",
"model",
"while",
"free",
"the",
"predictions",
"global"
],
[
"well",
"to",
"such",
"as",
"or",
"through",
"known",
"using",
"processing",
"these"
],
[
"compared",
"accuracy",
"dataset",
"results",
"with",
"high",
"respect",
"while",
"low",
"all"
],
[
"noise",
"large",
"propose",
"proposed",
"method",
"optimization",
"space",
"has",
"the",
"low"
],
[
"trained",
"training",
"loss",
"set",
"we",
"during",
"pre",
"federated",
"train",
"large"
],
[
"this",
"paper",
"work",
"new",
"process",
"approach",
"challenge",
"presents",
"strategies",
"approaches"
],
[
"ml",
"such",
"how",
"human",
"quantum",
"machine",
"system",
"systems",
"techniques",
"user"
],
[
"have",
"the",
"it",
"recent",
"these",
"but",
"been",
"not",
"has",
"however"
],
[
"it",
"be",
"can",
"used",
"how",
"or",
"they",
"at",
"to",
"the"
],
[
"where",
"are",
"the",
"these",
"they",
"when",
"more",
"their",
"some",
"different"
],
[
"node",
"and",
"graph",
"graphs",
"representation",
"the",
"nodes",
"gnns",
"we",
"structure"
],
[
"accuracy",
"classifier",
"classification",
"class",
"high",
"label",
"state",
"the",
"classifiers",
"art"
],
[
"models",
"trained",
"language",
"fidelity",
"generative",
"these",
"inference",
"at",
"modeling",
"high"
],
[
"approach",
"the",
"framework",
"3d",
"technique",
"an",
"its",
"input",
"using",
"high"
],
[
"our",
"approach",
"framework",
"results",
"state",
"first",
"art",
"best",
"work",
"experiments"
],
[
"algorithm",
"the",
"to",
"proposed",
"sample",
"matrix",
"regret",
"exchange",
"clustering",
"selection"
],
[
"between",
"set",
"from",
"language",
"information",
"using",
"different",
"the",
"object",
"representations"
],
[
"flow",
"the",
"propose",
"on",
"proposed",
"information",
"based",
"approaches",
"user",
"state"
],
[
"series",
"real",
"forecasting",
"at",
"time",
"world",
"temporal",
"of",
"state",
"hierarchical"
],
[
"datasets",
"many",
"other",
"both",
"existing",
"methods",
"linear",
"large",
"several",
"problems"
],
[
"it",
"is",
"one",
"the",
"where",
"than",
"at",
"when",
"its",
"information"
],
[
"segmentation",
"gan",
"generative",
"image",
"text",
"images",
"resolution",
"we",
"visual",
"trained"
],
[
"architectures",
"and",
"convolutional",
"networks",
"neural",
"over",
"have",
"more",
"cnns",
"physics"
],
[
"against",
"to",
"and",
"adversarial",
"of",
"the",
"robust",
"attack",
"robustness",
"attacks"
],
[
"using",
"in",
"detect",
"attacks",
"the",
"detection",
"object",
"rate",
"dataset",
"system"
],
[
"policy",
"the",
"rl",
"agent",
"reinforcement",
"state",
"action",
"agents",
"policies",
"reward"
],
[
"learning",
"ensemble",
"results",
"recognition",
"deep",
"accuracy",
"speech",
"in",
"paper",
"design"
],
[
"convex",
"optimization",
"descent",
"function",
"problems",
"order",
"non",
"stochastic",
"gradient",
"convergence"
],
[
"achieve",
"performance",
"state",
"of",
"better",
"cost",
"proposed",
"art",
"design",
"different"
],
[
"than",
"other",
"more",
"two",
"algorithms",
"these",
"problems",
"variance",
"search",
"the"
],
[
"and",
"features",
"feature",
"the",
"selection",
"information",
"to",
"attention",
"interaction",
"speech"
],
[
"not",
"of",
"novel",
"only",
"two",
"propose",
"which",
"to",
"local",
"framework"
],
[
"distributions",
"distribution",
"estimation",
"over",
"samples",
"bayesian",
"sample",
"bounds",
"variational",
"inference"
],
[
"rank",
"problem",
"matrix",
"optimal",
"the",
"problems",
"solution",
"optimization",
"low",
"given"
],
[
"the",
"between",
"by",
"approach",
"into",
"filter",
"function",
"while",
"images",
"dimensional"
],
[
"tasks",
"task",
"to",
"language",
"meta",
"knowledge",
"different",
"multi",
"each",
"representations"
],
[
"the",
"knowledge",
"domains",
"domain",
"source",
"target",
"transfer",
"to",
"adaptation",
"proposed"
]
] | 6.245105 | all-MiniLM-L6-v2 | 0.59 | 0.007457 | 0.150291 | 0.68471 |
ArXiv ML Papers | NMF | 45 | 50 | [
[
"of",
"between",
"proposed",
"results",
"error",
"the",
"was",
"strategy",
"conditions",
"one"
],
[
"often",
"able",
"use",
"approach",
"to",
"their",
"compared",
"due",
"framework",
"ability"
],
[
"analysis",
"terms",
"of",
"set",
"large",
"art",
"these",
"number",
"and",
"state"
],
[
"research",
"and",
"study",
"including",
"both",
"two",
"between",
"respectively",
"their",
"various"
],
[
"introduce",
"new",
"study",
"provide",
"present",
"propose",
"we",
"show",
"use",
"demonstrate"
],
[
"addition",
"terms",
"work",
"or",
"many",
"particular",
"in",
"context",
"computational",
"applications"
],
[
"test",
"used",
"for",
"one",
"also",
"analysis",
"large",
"fast",
"each",
"useful"
],
[
"driven",
"synthetic",
"real",
"high",
"classification",
"large",
"data",
"labeled",
"techniques",
"framework"
],
[
"of",
"learning",
"supervised",
"metric",
"machine",
"to",
"learn",
"framework",
"active",
"self"
],
[
"where",
"than",
"one",
"its",
"it",
"is",
"the",
"when",
"and",
"at"
],
[
"datasets",
"on",
"work",
"or",
"rely",
"focus",
"previous",
"all",
"trained",
"real"
],
[
"where",
"when",
"more",
"some",
"their",
"are",
"different",
"they",
"these",
"new"
],
[
"neural",
"network",
"structure",
"show",
"layer",
"architecture",
"convolutional",
"nodes",
"learned",
"to"
],
[
"trained",
"model",
"the",
"parameters",
"inference",
"predictions",
"while",
"free",
"train",
"global"
],
[
"or",
"using",
"well",
"as",
"to",
"through",
"such",
"known",
"processing",
"these"
],
[
"with",
"accuracy",
"compared",
"high",
"results",
"dataset",
"respect",
"all",
"while",
"low"
],
[
"has",
"the",
"which",
"not",
"propose",
"novel",
"two",
"of",
"only",
"most"
],
[
"trained",
"we",
"loss",
"training",
"during",
"set",
"pre",
"datasets",
"train",
"federated"
],
[
"how",
"they",
"used",
"or",
"be",
"can",
"it",
"at",
"any",
"show"
],
[
"these",
"but",
"it",
"the",
"been",
"not",
"have",
"has",
"using",
"recent"
],
[
"process",
"work",
"approach",
"paper",
"the",
"this",
"new",
"presents",
"challenge",
"strategies"
],
[
"ml",
"how",
"system",
"machine",
"human",
"systems",
"quantum",
"such",
"techniques",
"research"
],
[
"by",
"approach",
"between",
"the",
"into",
"high",
"filter",
"while",
"function",
"images"
],
[
"node",
"and",
"the",
"graph",
"graphs",
"nodes",
"representation",
"we",
"gnns",
"structure"
],
[
"show",
"than",
"results",
"not",
"that",
"more",
"representations",
"find",
"also",
"such"
],
[
"inference",
"generative",
"language",
"models",
"surrogate",
"modeling",
"at",
"fidelity",
"trained",
"these"
],
[
"input",
"an",
"its",
"technique",
"the",
"approach",
"framework",
"high",
"3d",
"shape"
],
[
"approach",
"experiments",
"state",
"our",
"results",
"first",
"best",
"art",
"work",
"framework"
],
[
"algorithm",
"to",
"the",
"proposed",
"sample",
"matrix",
"regret",
"exchange",
"clustering",
"selection"
],
[
"using",
"between",
"language",
"set",
"from",
"different",
"object",
"features",
"representations",
"derived"
],
[
"flow",
"proposed",
"is",
"approaches",
"based",
"on",
"user",
"the",
"propose",
"state"
],
[
"time",
"at",
"real",
"series",
"forecasting",
"of",
"temporal",
"world",
"state",
"each"
],
[
"existing",
"datasets",
"methods",
"other",
"the",
"both",
"many",
"linear",
"art",
"large"
],
[
"task",
"different",
"meta",
"knowledge",
"language",
"tasks",
"representations",
"multi",
"the",
"trained"
],
[
"image",
"resolution",
"images",
"segmentation",
"gan",
"text",
"generative",
"we",
"visual",
"trained"
],
[
"neural",
"networks",
"convolutional",
"and",
"over",
"architectures",
"have",
"cnns",
"more",
"physics"
],
[
"robustness",
"the",
"attack",
"attacks",
"to",
"of",
"against",
"adversarial",
"and",
"robust"
],
[
"performance",
"art",
"state",
"of",
"the",
"achieve",
"better",
"proposed",
"cost",
"high"
],
[
"rl",
"agent",
"the",
"policy",
"reinforcement",
"reward",
"state",
"action",
"policies",
"agents"
],
[
"classification",
"the",
"feature",
"to",
"features",
"and",
"selection",
"of",
"speech",
"attention"
],
[
"stochastic",
"function",
"gradient",
"convex",
"convergence",
"optimization",
"problems",
"descent",
"order",
"non"
],
[
"to",
"learning",
"deep",
"ensemble",
"results",
"recognition",
"accuracy",
"speech",
"paper",
"design"
],
[
"the",
"algorithms",
"search",
"two",
"other",
"problems",
"variance",
"to",
"more",
"than"
],
[
"prediction",
"is",
"speed",
"accuracy",
"two",
"of",
"traffic",
"dataset",
"market",
"and"
],
[
"optimal",
"problem",
"problems",
"matrix",
"rank",
"solution",
"optimization",
"given",
"linear",
"low"
],
[
"over",
"samples",
"bayesian",
"estimation",
"distributions",
"distribution",
"sample",
"bounds",
"variational",
"inference"
],
[
"propose",
"the",
"proposed",
"noise",
"method",
"large",
"space",
"has",
"optimization",
"low"
],
[
"detection",
"using",
"classification",
"object",
"classifier",
"accuracy",
"dataset",
"detect",
"in",
"rate"
],
[
"each",
"the",
"between",
"information",
"channel",
"two",
"multi",
"user",
"local",
"semantic"
],
[
"target",
"domain",
"the",
"source",
"transfer",
"knowledge",
"adaptation",
"domains",
"to",
"datasets"
]
] | 6.301346 | all-MiniLM-L6-v2 | 0.58 | 0.009219 | 0.151183 | 0.683603 |
ArXiv ML Papers | NMF | 46 | 50 | [
[
"two",
"results",
"between",
"proposed",
"error",
"the",
"of",
"information",
"test",
"was"
],
[
"framework",
"their",
"use",
"due",
"to",
"able",
"compared",
"approach",
"often",
"ability"
],
[
"set",
"analysis",
"and",
"these",
"terms",
"number",
"of",
"study",
"parameters",
"different"
],
[
"two",
"and",
"both",
"including",
"between",
"study",
"research",
"respectively",
"various",
"present"
],
[
"these",
"we",
"study",
"new",
"show",
"demonstrate",
"propose",
"provide",
"present",
"use"
],
[
"work",
"particular",
"or",
"terms",
"in",
"context",
"space",
"addition",
"many",
"order"
],
[
"one",
"each",
"also",
"analysis",
"test",
"regression",
"for",
"used",
"day",
"specific"
],
[
"synthetic",
"data",
"driven",
"process",
"techniques",
"real",
"sets",
"framework",
"privacy",
"labeled"
],
[
"learning",
"of",
"framework",
"supervised",
"machine",
"metric",
"learn",
"active",
"reinforcement",
"has"
],
[
"show",
"more",
"that",
"than",
"not",
"find",
"also",
"results",
"prior",
"representations"
],
[
"work",
"previous",
"focus",
"rely",
"or",
"on",
"all",
"user",
"trained",
"provide"
],
[
"when",
"where",
"more",
"are",
"these",
"they",
"algorithms",
"their",
"some",
"new"
],
[
"nodes",
"convolutional",
"layer",
"prediction",
"show",
"network",
"structure",
"architecture",
"learned",
"neural"
],
[
"free",
"trained",
"parameters",
"privacy",
"uncertainty",
"model",
"predictions",
"the",
"while",
"inference"
],
[
"as",
"such",
"well",
"using",
"processing",
"the",
"through",
"or",
"to",
"known"
],
[
"compared",
"with",
"accuracy",
"results",
"respect",
"all",
"while",
"level",
"multiple",
"parameters"
],
[
"propose",
"noise",
"the",
"has",
"method",
"prediction",
"proposed",
"optimization",
"new",
"effectiveness"
],
[
"training",
"loss",
"trained",
"we",
"during",
"pre",
"set",
"federated",
"train",
"generalization"
],
[
"presents",
"work",
"approach",
"challenge",
"paper",
"process",
"new",
"this",
"distribution",
"strategies"
],
[
"machine",
"system",
"systems",
"quantum",
"such",
"ml",
"human",
"the",
"techniques",
"how"
],
[
"these",
"not",
"but",
"has",
"the",
"it",
"have",
"been",
"recent",
"however"
],
[
"the",
"be",
"can",
"to",
"used",
"how",
"it",
"they",
"or",
"any"
],
[
"into",
"between",
"the",
"approach",
"by",
"filter",
"while",
"however",
"function",
"inspired"
],
[
"graph",
"the",
"and",
"we",
"node",
"gnns",
"graphs",
"nodes",
"representation",
"structure"
],
[
"samples",
"datasets",
"scale",
"over",
"real",
"accuracy",
"world",
"dataset",
"large",
"the"
],
[
"models",
"fidelity",
"generative",
"inference",
"prediction",
"high",
"language",
"trained",
"these",
"modeling"
],
[
"approach",
"input",
"an",
"its",
"framework",
"shape",
"technique",
"3d",
"using",
"the"
],
[
"results",
"our",
"approach",
"best",
"framework",
"first",
"we",
"work",
"experiments",
"only"
],
[
"the",
"to",
"algorithm",
"algorithms",
"clustering",
"proposed",
"sample",
"label",
"two",
"linear"
],
[
"language",
"using",
"information",
"object",
"from",
"between",
"different",
"set",
"representations",
"the"
],
[
"information",
"proposed",
"is",
"user",
"flow",
"based",
"the",
"propose",
"approaches",
"better"
],
[
"time",
"series",
"real",
"at",
"of",
"temporal",
"forecasting",
"world",
"each",
"hierarchical"
],
[
"both",
"other",
"existing",
"methods",
"problems",
"many",
"different",
"be",
"linear",
"several"
],
[
"it",
"the",
"is",
"one",
"where",
"when",
"at",
"than",
"its",
"information"
],
[
"segmentation",
"images",
"gan",
"image",
"text",
"generative",
"resolution",
"we",
"trained",
"visual"
],
[
"networks",
"have",
"weights",
"physics",
"convolutional",
"over",
"and",
"cnns",
"architectures",
"neural"
],
[
"against",
"the",
"perturbations",
"robust",
"robustness",
"and",
"attack",
"attacks",
"adversarial",
"to"
],
[
"approaches",
"state",
"we",
"previous",
"propose",
"art",
"action",
"results",
"the",
"of"
],
[
"reward",
"agent",
"reinforcement",
"the",
"policy",
"policies",
"rl",
"agents",
"and",
"environment"
],
[
"deep",
"learning",
"recognition",
"accuracy",
"ensemble",
"results",
"paper",
"speech",
"in",
"design"
],
[
"convergence",
"problems",
"optimization",
"descent",
"function",
"gradient",
"convex",
"stochastic",
"order",
"non"
],
[
"achieve",
"cost",
"performance",
"different",
"to",
"better",
"design",
"their",
"computing",
"high"
],
[
"domain",
"to",
"transfer",
"domains",
"adaptation",
"target",
"knowledge",
"the",
"source",
"proposed"
],
[
"information",
"to",
"features",
"interaction",
"feature",
"selection",
"and",
"speech",
"the",
"attention"
],
[
"accuracy",
"classification",
"of",
"classifier",
"classes",
"classifiers",
"high",
"using",
"class",
"label"
],
[
"language",
"task",
"multi",
"to",
"tasks",
"different",
"knowledge",
"prediction",
"each",
"meta"
],
[
"detection",
"using",
"object",
"the",
"in",
"detect",
"rate",
"attacks",
"system",
"dataset"
],
[
"linear",
"samples",
"given",
"where",
"problems",
"the",
"optimal",
"solution",
"to",
"problem"
],
[
"to",
"which",
"not",
"only",
"propose",
"of",
"two",
"distribution",
"local",
"has"
],
[
"matrix",
"and",
"rank",
"low",
"completion",
"tensor",
"alternating",
"approximation",
"measurements",
"high"
]
] | 6.587935 | all-MiniLM-L6-v2 | 0.586 | 0.010488 | 0.153716 | 0.689687 |
ArXiv ML Papers | LDA | 43 | 10 | [
[
"of",
"to",
"the",
"in",
"and",
"we",
"is",
"for",
"learning",
"that"
],
[
"and",
"the",
"that",
"of",
"for",
"in",
"we",
"is",
"to",
"on"
],
[
"image",
"generative",
"gan",
"gans",
"and",
"the",
"generator",
"of",
"generation",
"adversarial"
],
[
"to",
"in",
"and",
"of",
"the",
"is",
"this",
"data",
"we",
"learning"
],
[
"the",
"to",
"time",
"series",
"forecasting",
"we",
"and",
"that",
"of",
"methods"
],
[
"in",
"on",
"is",
"and",
"the",
"we",
"for",
"to",
"of",
"this"
],
[
"to",
"of",
"the",
"and",
"is",
"in",
"we",
"for",
"that",
"on"
],
[
"the",
"we",
"of",
"and",
"in",
"to",
"learning",
"on",
"with",
"that"
],
[
"and",
"we",
"for",
"the",
"of",
"is",
"to",
"in",
"that",
"with"
],
[
"the",
"we",
"and",
"to",
"of",
"in",
"on",
"is",
"that",
"network"
]
] | 10.928315 | all-MiniLM-L6-v2 | 0.26 | -0.028659 | 0.262092 | 0.52677 |
ArXiv ML Papers | LDA | 44 | 10 | [
[
"of",
"the",
"series",
"and",
"time",
"to",
"in",
"we",
"for",
"models"
],
[
"on",
"and",
"is",
"we",
"to",
"in",
"the",
"of",
"with",
"for"
],
[
"to",
"for",
"graph",
"in",
"of",
"we",
"that",
"the",
"and",
"on"
],
[
"the",
"of",
"and",
"algorithm",
"gradient",
"is",
"for",
"convergence",
"in",
"we"
],
[
"we",
"attacks",
"of",
"training",
"to",
"adversarial",
"in",
"that",
"the",
"and"
],
[
"hardware",
"kernel",
"distillation",
"kernels",
"precision",
"quantization",
"student",
"bit",
"teacher",
"and"
],
[
"in",
"we",
"is",
"the",
"of",
"to",
"and",
"for",
"that",
"on"
],
[
"of",
"the",
"is",
"to",
"learning",
"for",
"in",
"this",
"and",
"we"
],
[
"of",
"to",
"the",
"that",
"in",
"is",
"learning",
"and",
"we",
"this"
],
[
"the",
"on",
"of",
"we",
"and",
"in",
"to",
"for",
"model",
"with"
]
] | 11.216451 | all-MiniLM-L6-v2 | 0.33 | -0.048647 | 0.253008 | 0.557253 |
ArXiv ML Papers | LDA | 45 | 10 | [
[
"we",
"on",
"that",
"the",
"and",
"of",
"graph",
"to",
"in",
"is"
],
[
"the",
"we",
"of",
"to",
"and",
"in",
"learning",
"that",
"this",
"on"
],
[
"on",
"for",
"to",
"of",
"and",
"in",
"the",
"we",
"is",
"learning"
],
[
"of",
"in",
"the",
"to",
"is",
"and",
"we",
"this",
"on",
"for"
],
[
"we",
"the",
"and",
"in",
"to",
"of",
"that",
"for",
"neural",
"as"
],
[
"user",
"on",
"the",
"to",
"recommendation",
"of",
"and",
"for",
"users",
"we"
],
[
"generator",
"resolution",
"gan",
"generative",
"gans",
"and",
"images",
"image",
"generation",
"we"
],
[
"of",
"the",
"and",
"for",
"is",
"we",
"to",
"in",
"on",
"data"
],
[
"flow",
"encoder",
"flows",
"traffic",
"translation",
"decoder",
"level",
"speed",
"normalizing",
"character"
],
[
"the",
"and",
"of",
"to",
"is",
"in",
"we",
"for",
"that",
"with"
]
] | 19.839619 | all-MiniLM-L6-v2 | 0.38 | -0.04485 | 0.241503 | 0.567928 |
ArXiv ML Papers | LDA | 46 | 10 | [
[
"to",
"in",
"and",
"of",
"the",
"we",
"is",
"neural",
"for",
"on"
],
[
"we",
"adversarial",
"of",
"the",
"and",
"to",
"attacks",
"training",
"that",
"on"
],
[
"the",
"of",
"to",
"and",
"in",
"on",
"with",
"for",
"feature",
"we"
],
[
"that",
"in",
"we",
"the",
"of",
"to",
"and",
"for",
"learning",
"this"
],
[
"algorithm",
"power",
"coupled",
"quantum",
"derived",
"shift",
"stability",
"hilbert",
"mode",
"under"
],
[
"in",
"and",
"the",
"of",
"is",
"to",
"this",
"we",
"for",
"as"
],
[
"in",
"of",
"the",
"to",
"and",
"we",
"for",
"is",
"that",
"data"
],
[
"the",
"of",
"we",
"and",
"in",
"to",
"is",
"for",
"that",
"with"
],
[
"and",
"we",
"that",
"the",
"of",
"on",
"in",
"to",
"our",
"this"
],
[
"traffic",
"driving",
"of",
"and",
"the",
"demand",
"is",
"with",
"for",
"are"
]
] | 10.933623 | all-MiniLM-L6-v2 | 0.35 | -0.055283 | 0.261051 | 0.565739 |
ArXiv ML Papers | LDA | 43 | 20 | [
[
"to",
"in",
"learning",
"the",
"of",
"data",
"is",
"and",
"communication",
"model"
],
[
"and",
"network",
"neural",
"of",
"to",
"the",
"networks",
"we",
"in",
"that"
],
[
"image",
"generator",
"gans",
"likelihood",
"generative",
"gan",
"adversarial",
"and",
"the",
"images"
],
[
"quantum",
"traffic",
"the",
"to",
"and",
"is",
"driving",
"vehicle",
"in",
"of"
],
[
"sensitivity",
"of",
"subspace",
"construction",
"the",
"subspaces",
"in",
"is",
"to",
"mathematical"
],
[
"to",
"and",
"the",
"of",
"in",
"we",
"that",
"adversarial",
"training",
"attacks"
],
[
"and",
"for",
"this",
"we",
"of",
"to",
"in",
"is",
"that",
"the"
],
[
"learning",
"the",
"methods",
"data",
"labels",
"supervised",
"semi",
"and",
"labeled",
"label"
],
[
"to",
"of",
"in",
"the",
"that",
"and",
"ml",
"can",
"we",
"for"
],
[
"and",
"the",
"we",
"of",
"on",
"to",
"is",
"network",
"in",
"graph"
],
[
"to",
"in",
"we",
"for",
"that",
"the",
"is",
"policy",
"of",
"and"
],
[
"of",
"and",
"in",
"the",
"is",
"we",
"on",
"that",
"for",
"to"
],
[
"of",
"in",
"and",
"the",
"that",
"to",
"we",
"learning",
"for",
"is"
],
[
"we",
"to",
"on",
"the",
"of",
"and",
"in",
"for",
"model",
"with"
],
[
"in",
"of",
"the",
"and",
"with",
"we",
"to",
"for",
"this",
"is"
],
[
"and",
"classification",
"of",
"to",
"data",
"the",
"classifier",
"for",
"in",
"is"
],
[
"this",
"we",
"the",
"of",
"and",
"for",
"in",
"to",
"is",
"on"
],
[
"trees",
"stochastic",
"varepsilon",
"left",
"right",
"frac",
"convergence",
"rate",
"optimal",
"oracle"
],
[
"to",
"we",
"that",
"the",
"on",
"for",
"learning",
"of",
"and",
"data"
],
[
"data",
"tensor",
"and",
"the",
"video",
"metric",
"to",
"for",
"of",
"learning"
]
] | 12.112303 | all-MiniLM-L6-v2 | 0.315 | -0.023955 | 0.215658 | 0.56059 |
ArXiv ML Papers | LDA | 44 | 20 | [
[
"to",
"series",
"time",
"of",
"and",
"the",
"in",
"video",
"we",
"for"
],
[
"the",
"to",
"and",
"of",
"in",
"for",
"we",
"is",
"on",
"with"
],
[
"and",
"the",
"to",
"of",
"in",
"we",
"data",
"is",
"that",
"this"
],
[
"search",
"and",
"the",
"variables",
"variational",
"of",
"autoencoders",
"vae",
"generative",
"is"
],
[
"adversarial",
"to",
"of",
"we",
"attacks",
"the",
"that",
"attack",
"training",
"robustness"
],
[
"kernel",
"bayesian",
"gaussian",
"posterior",
"processes",
"the",
"generalization",
"kernels",
"compositional",
"and"
],
[
"in",
"the",
"is",
"we",
"and",
"of",
"to",
"that",
"model",
"this"
],
[
"of",
"the",
"and",
"to",
"is",
"in",
"for",
"this",
"be",
"machine"
],
[
"in",
"policy",
"of",
"and",
"learning",
"to",
"the",
"that",
"we",
"reinforcement"
],
[
"and",
"speech",
"was",
"to",
"model",
"the",
"for",
"in",
"of",
"signals"
],
[
"of",
"the",
"and",
"to",
"for",
"graph",
"in",
"on",
"that",
"we"
],
[
"and",
"interactive",
"users",
"of",
"sensing",
"user",
"to",
"for",
"data",
"that"
],
[
"answer",
"black",
"query",
"box",
"question",
"questions",
"system",
"rate",
"answering",
"based"
],
[
"of",
"the",
"and",
"we",
"in",
"data",
"to",
"learning",
"is",
"on"
],
[
"of",
"the",
"we",
"to",
"in",
"and",
"that",
"is",
"for",
"with"
],
[
"xgboost",
"bandits",
"and",
"approaches",
"for",
"in",
"are",
"using",
"text",
"proposal"
],
[
"in",
"learning",
"on",
"this",
"for",
"the",
"of",
"we",
"to",
"and"
],
[
"to",
"of",
"the",
"by",
"for",
"cnn",
"resolution",
"and",
"neural",
"with"
],
[
"of",
"we",
"in",
"to",
"for",
"and",
"the",
"is",
"that",
"algorithm"
],
[
"to",
"and",
"we",
"the",
"in",
"that",
"of",
"for",
"is",
"learning"
]
] | 21.244746 | all-MiniLM-L6-v2 | 0.355 | -0.032252 | 0.226536 | 0.583911 |
ArXiv ML Papers | LDA | 45 | 20 | [
[
"on",
"and",
"the",
"in",
"to",
"we",
"is",
"of",
"that",
"for"
],
[
"the",
"to",
"of",
"we",
"and",
"in",
"learning",
"that",
"on",
"for"
],
[
"and",
"to",
"target",
"we",
"the",
"of",
"in",
"learning",
"domain",
"for"
],
[
"of",
"the",
"to",
"and",
"we",
"in",
"learning",
"is",
"this",
"as"
],
[
"of",
"to",
"the",
"in",
"and",
"we",
"as",
"for",
"learning",
"that"
],
[
"of",
"user",
"users",
"the",
"and",
"recommendation",
"to",
"for",
"we",
"items"
],
[
"generative",
"to",
"resolution",
"images",
"gan",
"and",
"image",
"we",
"gans",
"generator"
],
[
"is",
"the",
"and",
"for",
"in",
"of",
"to",
"data",
"we",
"on"
],
[
"mild",
"actively",
"suggested",
"integrating",
"poisoning",
"regardless",
"growth",
"devise",
"existence",
"conclusion"
],
[
"the",
"of",
"is",
"and",
"in",
"we",
"to",
"for",
"that",
"with"
],
[
"the",
"text",
"and",
"of",
"in",
"language",
"attention",
"with",
"recurrent",
"neural"
],
[
"and",
"we",
"to",
"fairness",
"that",
"fair",
"of",
"functional",
"are",
"this"
],
[
"of",
"to",
"the",
"graph",
"and",
"we",
"in",
"that",
"on",
"graphs"
],
[
"explanations",
"protein",
"speaker",
"to",
"and",
"the",
"of",
"hardware",
"feature",
"that"
],
[
"activation",
"layer",
"scaling",
"network",
"networks",
"neural",
"functions",
"the",
"of",
"depth"
],
[
"to",
"is",
"in",
"on",
"the",
"and",
"of",
"for",
"we",
"training"
],
[
"of",
"and",
"we",
"the",
"to",
"in",
"an",
"object",
"is",
"for"
],
[
"of",
"to",
"the",
"learning",
"we",
"and",
"supervised",
"data",
"that",
"on"
],
[
"and",
"is",
"of",
"in",
"to",
"the",
"for",
"we",
"this",
"on"
],
[
"the",
"of",
"to",
"and",
"training",
"that",
"we",
"adversarial",
"in",
"attacks"
]
] | 20.694685 | all-MiniLM-L6-v2 | 0.335 | -0.048407 | 0.212405 | 0.557579 |
ArXiv ML Papers | LDA | 46 | 20 | [
[
"network",
"the",
"to",
"of",
"we",
"in",
"neural",
"networks",
"and",
"on"
],
[
"the",
"of",
"and",
"to",
"for",
"with",
"on",
"we",
"is",
"that"
],
[
"on",
"of",
"and",
"the",
"with",
"in",
"to",
"for",
"we",
"is"
],
[
"in",
"of",
"and",
"we",
"learning",
"to",
"the",
"that",
"graphs",
"for"
],
[
"in",
"this",
"for",
"quantum",
"noise",
"and",
"is",
"stability",
"the",
"power"
],
[
"for",
"is",
"to",
"of",
"this",
"the",
"and",
"as",
"in",
"we"
],
[
"in",
"of",
"to",
"we",
"is",
"the",
"and",
"for",
"that",
"data"
],
[
"for",
"to",
"is",
"that",
"of",
"we",
"and",
"the",
"in",
"with"
],
[
"and",
"on",
"of",
"to",
"in",
"we",
"the",
"that",
"our",
"this"
],
[
"are",
"driving",
"and",
"the",
"service",
"expert",
"pooling",
"of",
"with",
"experts"
],
[
"diffusion",
"equations",
"traffic",
"equation",
"neural",
"speed",
"network",
"solution",
"mathematical",
"the"
],
[
"policy",
"for",
"and",
"of",
"gradient",
"to",
"the",
"we",
"in",
"learning"
],
[
"the",
"and",
"for",
"in",
"of",
"to",
"we",
"domain",
"models",
"speech"
],
[
"data",
"learning",
"trees",
"metric",
"distance",
"the",
"conditional",
"from",
"distances",
"we"
],
[
"of",
"and",
"to",
"supervised",
"in",
"learning",
"the",
"data",
"we",
"with"
],
[
"graph",
"we",
"embedding",
"node",
"nodes",
"the",
"to",
"representations",
"embeddings",
"representation"
],
[
"dramatically",
"greatly",
"newton",
"concrete",
"exhibits",
"proof",
"reconstructing",
"decreases",
"caused",
"asymptotically"
],
[
"concepts",
"words",
"word",
"vectors",
"to",
"promising",
"vector",
"we",
"similarity",
"this"
],
[
"to",
"attacks",
"of",
"adversarial",
"we",
"and",
"that",
"the",
"in",
"model"
],
[
"to",
"of",
"the",
"in",
"and",
"for",
"we",
"detection",
"samples",
"with"
]
] | 11.831478 | all-MiniLM-L6-v2 | 0.39 | -0.050428 | 0.208954 | 0.586658 |
ArXiv ML Papers | LDA | 43 | 30 | [
[
"to",
"is",
"learning",
"the",
"and",
"of",
"in",
"transfer",
"with",
"supervised"
],
[
"neural",
"network",
"the",
"of",
"networks",
"to",
"and",
"speech",
"layer",
"this"
],
[
"generalization",
"as",
"functions",
"tasks",
"synthesis",
"log",
"which",
"negative",
"loss",
"generalized"
],
[
"and",
"to",
"of",
"data",
"traffic",
"learning",
"policy",
"reinforcement",
"the",
"stream"
],
[
"of",
"robust",
"pooling",
"consumption",
"in",
"algorithms",
"case",
"the",
"energy",
"and"
],
[
"the",
"of",
"attacks",
"in",
"training",
"to",
"we",
"adversarial",
"and",
"that"
],
[
"we",
"to",
"the",
"in",
"of",
"that",
"and",
"is",
"this",
"for"
],
[
"face",
"in",
"treatment",
"data",
"the",
"of",
"for",
"estimator",
"causal",
"bias"
],
[
"the",
"to",
"of",
"in",
"and",
"ml",
"that",
"can",
"we",
"be"
],
[
"and",
"to",
"the",
"of",
"we",
"images",
"image",
"with",
"on",
"in"
],
[
"the",
"of",
"quantum",
"in",
"and",
"as",
"for",
"to",
"we",
"kernel"
],
[
"of",
"the",
"and",
"in",
"for",
"to",
"is",
"we",
"that",
"this"
],
[
"of",
"the",
"to",
"in",
"this",
"and",
"we",
"for",
"learning",
"that"
],
[
"in",
"the",
"we",
"to",
"and",
"learning",
"for",
"of",
"task",
"on"
],
[
"of",
"and",
"method",
"to",
"for",
"is",
"in",
"we",
"learning",
"the"
],
[
"of",
"feature",
"for",
"and",
"active",
"the",
"that",
"fairness",
"we",
"in"
],
[
"we",
"of",
"and",
"for",
"in",
"to",
"the",
"on",
"is",
"this"
],
[
"candidate",
"edge",
"frac",
"partitioning",
"nearest",
"partition",
"right",
"neighbor",
"neighbors",
"left"
],
[
"that",
"the",
"we",
"to",
"and",
"of",
"queries",
"on",
"clustering",
"learning"
],
[
"supervised",
"the",
"detection",
"data",
"to",
"and",
"for",
"from",
"we",
"of"
],
[
"of",
"to",
"the",
"and",
"is",
"we",
"in",
"on",
"with",
"that"
],
[
"source",
"domain",
"the",
"target",
"audio",
"video",
"cross",
"adaptation",
"net",
"videos"
],
[
"our",
"questions",
"we",
"shift",
"question",
"answer",
"framework",
"and",
"consistency",
"distributional"
],
[
"communication",
"data",
"of",
"the",
"privacy",
"users",
"federated",
"user",
"and",
"to"
],
[
"the",
"of",
"we",
"and",
"in",
"for",
"with",
"to",
"that",
"is"
],
[
"of",
"to",
"distillation",
"teacher",
"and",
"in",
"the",
"student",
"knowledge",
"network"
],
[
"and",
"to",
"that",
"of",
"for",
"the",
"in",
"networks",
"we",
"on"
],
[
"generation",
"text",
"image",
"generative",
"to",
"sequence",
"generate",
"models",
"the",
"and"
],
[
"music",
"recommendation",
"expert",
"contextual",
"context",
"knowledge",
"regret",
"scenarios",
"real",
"bandit"
],
[
"of",
"the",
"graphs",
"to",
"and",
"graph",
"we",
"node",
"for",
"nodes"
]
] | 13.241964 | all-MiniLM-L6-v2 | 0.4 | -0.039303 | 0.200602 | 0.60234 |
ArXiv ML Papers | LDA | 44 | 30 | [
[
"in",
"we",
"deep",
"design",
"to",
"the",
"and",
"of",
"models",
"translation"
],
[
"in",
"and",
"the",
"we",
"of",
"is",
"on",
"to",
"for",
"network"
],
[
"to",
"of",
"and",
"in",
"graph",
"we",
"the",
"that",
"on",
"this"
],
[
"and",
"devices",
"the",
"communication",
"federated",
"model",
"channel",
"proposed",
"distributed",
"to"
],
[
"the",
"attacks",
"adversarial",
"attack",
"robustness",
"training",
"of",
"to",
"quantum",
"against"
],
[
"kernels",
"are",
"recursive",
"functions",
"gaussian",
"activation",
"width",
"compositional",
"mixed",
"kernel"
],
[
"in",
"of",
"to",
"we",
"the",
"and",
"that",
"this",
"on",
"is"
],
[
"in",
"the",
"of",
"and",
"to",
"for",
"can",
"this",
"systems",
"be"
],
[
"to",
"and",
"agent",
"tasks",
"of",
"in",
"that",
"the",
"learning",
"we"
],
[
"to",
"net",
"of",
"and",
"the",
"in",
"this",
"hardware",
"for",
"demand"
],
[
"is",
"that",
"and",
"the",
"in",
"to",
"of",
"we",
"on",
"network"
],
[
"sensing",
"of",
"free",
"user",
"motion",
"for",
"text",
"and",
"study",
"head"
],
[
"tree",
"on",
"the",
"learning",
"this",
"meaningful",
"trees",
"valued",
"is",
"averaged"
],
[
"of",
"we",
"to",
"data",
"and",
"is",
"learning",
"supervised",
"the",
"on"
],
[
"of",
"to",
"the",
"and",
"in",
"that",
"we",
"for",
"on",
"with"
],
[
"privacy",
"private",
"xgboost",
"differentially",
"data",
"learning",
"and",
"for",
"model",
"inference"
],
[
"in",
"for",
"and",
"to",
"of",
"the",
"we",
"data",
"learning",
"this"
],
[
"to",
"resolution",
"for",
"and",
"images",
"scale",
"of",
"the",
"by",
"games"
],
[
"the",
"of",
"we",
"to",
"for",
"and",
"that",
"in",
"algorithm",
"is"
],
[
"of",
"and",
"in",
"learning",
"we",
"that",
"the",
"to",
"for",
"is"
],
[
"to",
"the",
"of",
"and",
"data",
"for",
"is",
"that",
"in",
"we"
],
[
"to",
"in",
"metric",
"series",
"of",
"time",
"the",
"on",
"is",
"similarity"
],
[
"of",
"to",
"in",
"the",
"and",
"model",
"we",
"learning",
"policy",
"speech"
],
[
"optimization",
"method",
"the",
"black",
"and",
"box",
"we",
"gradient",
"problems",
"gans"
],
[
"bit",
"massive",
"diffusion",
"the",
"constrained",
"we",
"in",
"to",
"and",
"that"
],
[
"the",
"in",
"of",
"and",
"to",
"this",
"is",
"we",
"for",
"with"
],
[
"sampling",
"carlo",
"dl",
"monte",
"relevance",
"markov",
"multi",
"label",
"mcmc",
"chain"
],
[
"to",
"data",
"region",
"detection",
"proposal",
"boxes",
"object",
"interpolation",
"mapping",
"points"
],
[
"imbalance",
"arises",
"sampled",
"al",
"actively",
"detail",
"idea",
"corpus",
"imbalanced",
"overlapping"
],
[
"search",
"and",
"to",
"the",
"of",
"we",
"forecasting",
"for",
"neural",
"power"
]
] | 13.062546 | all-MiniLM-L6-v2 | 0.416667 | -0.046981 | 0.187724 | 0.604609 |
ArXiv ML Papers | LDA | 45 | 30 | [
[
"in",
"to",
"model",
"of",
"the",
"we",
"and",
"on",
"is",
"data"
],
[
"to",
"of",
"learning",
"that",
"we",
"the",
"and",
"policy",
"in",
"reinforcement"
],
[
"in",
"domain",
"and",
"transfer",
"to",
"source",
"target",
"the",
"of",
"for"
],
[
"is",
"the",
"to",
"of",
"method",
"this",
"and",
"in",
"fairness",
"from"
],
[
"time",
"for",
"in",
"of",
"and",
"series",
"the",
"to",
"forecasting",
"term"
],
[
"user",
"of",
"users",
"and",
"for",
"model",
"items",
"the",
"algorithms",
"to"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"of",
"and",
"the",
"to",
"in",
"for",
"is",
"on",
"data",
"with"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"the",
"for",
"algorithm",
"is",
"of",
"we",
"in",
"to",
"and",
"that"
],
[
"the",
"language",
"recurrent",
"translation",
"rnn",
"and",
"label",
"languages",
"multi",
"to"
],
[
"nearest",
"social",
"media",
"neighbor",
"diffusion",
"classifier",
"communities",
"topological",
"information",
"in"
],
[
"the",
"of",
"graph",
"clustering",
"and",
"we",
"nodes",
"to",
"node",
"for"
],
[
"speaker",
"to",
"end",
"hardware",
"differentiable",
"accelerators",
"forgetting",
"and",
"neural",
"catastrophic"
],
[
"structured",
"belief",
"partition",
"mean",
"approximations",
"field",
"counterpart",
"positive",
"trees",
"naive"
],
[
"the",
"of",
"and",
"in",
"we",
"training",
"to",
"is",
"on",
"with"
],
[
"the",
"channel",
"to",
"in",
"object",
"and",
"is",
"detection",
"of",
"attacks"
],
[
"the",
"learning",
"data",
"to",
"latent",
"supervised",
"of",
"we",
"metric",
"distance"
],
[
"the",
"of",
"and",
"to",
"is",
"for",
"in",
"queries",
"we",
"ensemble"
],
[
"in",
"to",
"of",
"the",
"with",
"we",
"and",
"is",
"for",
"neural"
],
[
"the",
"code",
"and",
"to",
"training",
"github",
"https",
"pre",
"com",
"networks"
],
[
"of",
"sequential",
"methods",
"for",
"solving",
"coupled",
"stochastic",
"framework",
"games",
"to"
],
[
"networks",
"random",
"of",
"that",
"layer",
"and",
"error",
"the",
"we",
"generalization"
],
[
"learning",
"in",
"data",
"deep",
"and",
"of",
"to",
"the",
"we",
"for"
],
[
"of",
"in",
"the",
"local",
"points",
"point",
"and",
"kernels",
"kernel",
"that"
],
[
"as",
"and",
"of",
"flow",
"the",
"to",
"learning",
"in",
"we",
"tree"
],
[
"the",
"and",
"of",
"to",
"graph",
"in",
"for",
"tasks",
"we",
"on"
],
[
"the",
"of",
"to",
"and",
"in",
"we",
"on",
"for",
"that",
"this"
],
[
"and",
"image",
"generative",
"the",
"images",
"we",
"to",
"text",
"gan",
"of"
],
[
"changing",
"processes",
"inefficient",
"on",
"conditions",
"process",
"rapidly",
"act",
"value",
"wide"
]
] | 12.018622 | all-MiniLM-L6-v2 | 0.443333 | -0.071942 | 0.169726 | 0.634942 |
ArXiv ML Papers | LDA | 46 | 30 | [
[
"the",
"and",
"to",
"of",
"in",
"neural",
"we",
"network",
"on",
"networks"
],
[
"is",
"for",
"the",
"in",
"and",
"we",
"our",
"to",
"of",
"with"
],
[
"the",
"and",
"in",
"of",
"on",
"to",
"data",
"image",
"we",
"is"
],
[
"learning",
"of",
"to",
"in",
"the",
"and",
"we",
"that",
"for",
"agents"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"the",
"of",
"and",
"in",
"to",
"is",
"for",
"flow",
"we",
"on"
],
[
"of",
"the",
"and",
"to",
"in",
"for",
"is",
"we",
"that",
"data"
],
[
"of",
"in",
"and",
"the",
"we",
"is",
"to",
"for",
"that",
"algorithm"
],
[
"to",
"the",
"image",
"of",
"we",
"on",
"images",
"and",
"that",
"in"
],
[
"the",
"and",
"segmentation",
"semantic",
"traffic",
"driving",
"of",
"in",
"expert",
"pooling"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"of",
"the",
"and",
"gradient",
"we",
"for",
"to",
"policy",
"optimization",
"in"
],
[
"we",
"of",
"to",
"the",
"and",
"models",
"that",
"for",
"our",
"object"
],
[
"label",
"multi",
"tensor",
"the",
"labels",
"query",
"and",
"relevance",
"algorithm",
"given"
],
[
"the",
"of",
"model",
"to",
"methods",
"and",
"ensemble",
"generative",
"data",
"we"
],
[
"nodes",
"graph",
"node",
"graphs",
"the",
"embedding",
"to",
"of",
"network",
"structure"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"of",
"to",
"the",
"that",
"we",
"attacks",
"adversarial",
"training",
"in",
"attack"
],
[
"of",
"for",
"in",
"energy",
"to",
"the",
"we",
"monte",
"and",
"carlo"
],
[
"fusion",
"video",
"feature",
"frame",
"frames",
"the",
"of",
"this",
"in",
"we"
],
[
"the",
"of",
"to",
"and",
"that",
"we",
"on",
"in",
"for",
"is"
],
[
"of",
"the",
"and",
"this",
"for",
"to",
"in",
"is",
"we",
"that"
],
[
"language",
"representations",
"words",
"in",
"of",
"speech",
"the",
"languages",
"we",
"embeddings"
],
[
"shift",
"clustering",
"and",
"distributional",
"algorithm",
"consistency",
"mode",
"we",
"consistent",
"under"
],
[
"of",
"and",
"the",
"we",
"inference",
"data",
"to",
"variational",
"latent",
"bayesian"
],
[
"is",
"in",
"to",
"we",
"learning",
"the",
"and",
"domain",
"of",
"target"
],
[
"feature",
"classifier",
"metric",
"features",
"classification",
"forecasting",
"the",
"filter",
"classifiers",
"of"
],
[
"the",
"to",
"that",
"of",
"robot",
"we",
"in",
"and",
"for",
"privacy"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
]
] | 12.875089 | all-MiniLM-L6-v2 | 0.336667 | -0.098042 | 0.191397 | 0.648427 |
ArXiv ML Papers | LDA | 43 | 40 | [
[
"the",
"for",
"is",
"in",
"of",
"distributed",
"and",
"to",
"communication",
"flow"
],
[
"to",
"the",
"speech",
"of",
"signals",
"and",
"text",
"in",
"we",
"word"
],
[
"loss",
"open",
"source",
"goals",
"and",
"gans",
"an",
"as",
"library",
"implementations"
],
[
"traffic",
"quantum",
"in",
"and",
"noise",
"the",
"of",
"to",
"is",
"data"
],
[
"reference",
"vs",
"2019",
"the",
"best",
"of",
"on",
"in",
"and",
"be"
],
[
"to",
"adversarial",
"in",
"and",
"of",
"the",
"attacks",
"we",
"training",
"models"
],
[
"for",
"the",
"we",
"is",
"in",
"that",
"and",
"of",
"to",
"this"
],
[
"system",
"systems",
"energy",
"control",
"and",
"physics",
"in",
"the",
"of",
"to"
],
[
"to",
"the",
"of",
"and",
"that",
"in",
"ml",
"is",
"we",
"reward"
],
[
"we",
"the",
"and",
"images",
"image",
"to",
"segmentation",
"of",
"in",
"with"
],
[
"driving",
"and",
"of",
"dnn",
"in",
"the",
"to",
"for",
"on",
"as"
],
[
"and",
"clustering",
"in",
"of",
"the",
"based",
"for",
"to",
"is",
"we"
],
[
"in",
"to",
"the",
"of",
"and",
"we",
"for",
"neural",
"on",
"networks"
],
[
"the",
"to",
"of",
"and",
"learning",
"we",
"in",
"tasks",
"that",
"task"
],
[
"is",
"for",
"and",
"in",
"of",
"to",
"we",
"the",
"this",
"method"
],
[
"of",
"for",
"short",
"protein",
"term",
"the",
"and",
"in",
"lstm",
"is"
],
[
"and",
"in",
"of",
"to",
"we",
"for",
"the",
"is",
"on",
"this"
],
[
"candidate",
"diversity",
"candidates",
"retrieval",
"user",
"mixture",
"set",
"recall",
"clusters",
"generation"
],
[
"the",
"to",
"that",
"and",
"private",
"privacy",
"of",
"clustering",
"in",
"we"
],
[
"is",
"and",
"we",
"to",
"of",
"the",
"data",
"causal",
"in",
"local"
],
[
"and",
"is",
"to",
"on",
"in",
"the",
"of",
"we",
"with",
"for"
],
[
"the",
"adaptation",
"source",
"domain",
"video",
"target",
"cross",
"fine",
"grained",
"videos"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"mixed",
"in",
"the",
"and",
"languages",
"of",
"to",
"compositional",
"for",
"kernels"
],
[
"in",
"the",
"of",
"is",
"to",
"for",
"algorithm",
"and",
"we",
"that"
],
[
"and",
"the",
"to",
"of",
"left",
"sparse",
"right",
"we",
"in",
"frac"
],
[
"to",
"in",
"with",
"and",
"that",
"of",
"performance",
"we",
"for",
"the"
],
[
"image",
"generation",
"the",
"and",
"generate",
"of",
"to",
"forecasting",
"for",
"text"
],
[
"activation",
"relu",
"functions",
"layers",
"neurons",
"neuron",
"complex",
"function",
"sign",
"unit"
],
[
"graph",
"the",
"and",
"to",
"for",
"graphs",
"of",
"we",
"embedding",
"that"
],
[
"and",
"to",
"in",
"the",
"of",
"we",
"that",
"as",
"is",
"demand"
],
[
"bounds",
"the",
"we",
"of",
"to",
"and",
"bayesian",
"with",
"for",
"optimization"
],
[
"and",
"for",
"we",
"to",
"in",
"classifier",
"of",
"the",
"learning",
"label"
],
[
"the",
"in",
"of",
"to",
"net",
"and",
"geometric",
"graph",
"convolutional",
"style"
],
[
"the",
"to",
"for",
"data",
"we",
"of",
"and",
"this",
"in",
"that"
],
[
"the",
"to",
"we",
"of",
"and",
"on",
"for",
"models",
"data",
"in"
],
[
"of",
"the",
"to",
"and",
"in",
"network",
"we",
"networks",
"object",
"on"
],
[
"gan",
"graph",
"the",
"generative",
"gnns",
"node",
"and",
"is",
"of",
"to"
],
[
"to",
"and",
"in",
"is",
"we",
"of",
"that",
"the",
"on",
"learning"
],
[
"and",
"of",
"policy",
"we",
"to",
"the",
"in",
"learning",
"for",
"that"
]
] | 12.933721 | all-MiniLM-L6-v2 | 0.3475 | -0.043755 | 0.195528 | 0.61362 |
ArXiv ML Papers | LDA | 44 | 40 | [
[
"of",
"to",
"the",
"we",
"manifold",
"in",
"for",
"neural",
"on",
"and"
],
[
"the",
"and",
"to",
"with",
"neural",
"in",
"of",
"on",
"we",
"for"
],
[
"data",
"we",
"on",
"of",
"the",
"that",
"and",
"this",
"in",
"to"
],
[
"the",
"dynamics",
"and",
"to",
"of",
"that",
"model",
"data",
"vae",
"groups"
],
[
"attack",
"attacks",
"adversarial",
"to",
"the",
"we",
"of",
"training",
"robustness",
"against"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"to",
"the",
"of",
"we",
"that",
"in",
"and",
"is",
"this",
"on"
],
[
"be",
"for",
"in",
"the",
"and",
"to",
"of",
"can",
"is",
"this"
],
[
"to",
"learning",
"the",
"of",
"in",
"that",
"policy",
"and",
"reinforcement",
"we"
],
[
"of",
"for",
"was",
"we",
"to",
"and",
"the",
"in",
"long",
"term"
],
[
"and",
"in",
"the",
"of",
"to",
"for",
"we",
"that",
"on",
"learning"
],
[
"word",
"of",
"head",
"smart",
"motion",
"text",
"natural",
"selection",
"for",
"accuracy"
],
[
"dynamical",
"processes",
"system",
"library",
"online",
"systems",
"the",
"production",
"an",
"to"
],
[
"the",
"to",
"of",
"and",
"learning",
"data",
"in",
"is",
"we",
"methods"
],
[
"for",
"and",
"the",
"on",
"of",
"that",
"we",
"to",
"in",
"is"
],
[
"for",
"deep",
"models",
"in",
"net",
"are",
"diffusion",
"and",
"paper",
"of"
],
[
"and",
"of",
"the",
"to",
"in",
"for",
"we",
"learning",
"this",
"on"
],
[
"is",
"the",
"and",
"cnn",
"to",
"dnn",
"resolution",
"by",
"for",
"of"
],
[
"of",
"we",
"to",
"that",
"for",
"the",
"gradient",
"in",
"and",
"algorithm"
],
[
"we",
"the",
"in",
"and",
"of",
"that",
"for",
"learning",
"to",
"is"
],
[
"for",
"we",
"and",
"to",
"in",
"of",
"the",
"is",
"data",
"that"
],
[
"time",
"series",
"and",
"of",
"in",
"the",
"for",
"to",
"we",
"is"
],
[
"speech",
"the",
"to",
"on",
"in",
"of",
"based",
"and",
"we",
"model"
],
[
"epsilon",
"method",
"we",
"optimization",
"black",
"box",
"and",
"gradient",
"query",
"propose"
],
[
"the",
"feature",
"classifier",
"class",
"classification",
"classes",
"data",
"and",
"we",
"of"
],
[
"and",
"for",
"we",
"of",
"to",
"is",
"the",
"with",
"in",
"on"
],
[
"carlo",
"recommendation",
"monte",
"the",
"and",
"chain",
"of",
"item",
"mcmc",
"parallel"
],
[
"self",
"contrastive",
"clustering",
"supervised",
"representations",
"in",
"transformations",
"data",
"to",
"augmentation"
],
[
"imbalanced",
"for",
"catastrophic",
"to",
"forgetting",
"the",
"event",
"imbalance",
"we",
"that"
],
[
"to",
"architecture",
"and",
"of",
"search",
"channel",
"the",
"with",
"is",
"for"
],
[
"the",
"in",
"and",
"of",
"to",
"is",
"we",
"for",
"that",
"problem"
],
[
"to",
"tensors",
"and",
"in",
"tensor",
"on",
"rank",
"networks",
"multiple",
"of"
],
[
"of",
"we",
"data",
"the",
"to",
"in",
"and",
"for",
"that",
"as"
],
[
"of",
"type",
"and",
"types",
"task",
"any",
"associated",
"answer",
"to",
"the"
],
[
"forecasting",
"of",
"and",
"forecast",
"the",
"probabilistic",
"we",
"demand",
"to",
"that"
],
[
"on",
"data",
"to",
"systems",
"for",
"is",
"of",
"and",
"the",
"this"
],
[
"the",
"of",
"graph",
"graphs",
"nodes",
"node",
"and",
"to",
"on",
"networks"
],
[
"to",
"in",
"and",
"we",
"of",
"the",
"are",
"for",
"is",
"that"
],
[
"of",
"the",
"and",
"to",
"approximately",
"sound",
"in",
"reliable",
"on",
"is"
],
[
"bias",
"of",
"in",
"to",
"the",
"and",
"nn",
"that",
"error",
"is"
]
] | 13.056187 | all-MiniLM-L6-v2 | 0.3425 | -0.037738 | 0.207822 | 0.592155 |
ArXiv ML Papers | LDA | 45 | 40 | [
[
"rank",
"the",
"and",
"matrix",
"of",
"tensor",
"we",
"is",
"for",
"in"
],
[
"with",
"of",
"learning",
"in",
"we",
"rl",
"to",
"the",
"that",
"and"
],
[
"domain",
"the",
"of",
"and",
"to",
"target",
"for",
"source",
"label",
"in"
],
[
"quantum",
"to",
"learning",
"is",
"and",
"the",
"of",
"this",
"in",
"for"
],
[
"for",
"of",
"and",
"the",
"time",
"forecasting",
"series",
"as",
"in",
"ensemble"
],
[
"the",
"items",
"recommendation",
"to",
"users",
"user",
"of",
"based",
"on",
"recommender"
],
[
"profiles",
"scale",
"details",
"variable",
"spatial",
"resolution",
"patches",
"images",
"at",
"conditioning"
],
[
"the",
"of",
"and",
"to",
"data",
"in",
"is",
"for",
"on",
"with"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"is",
"for",
"data",
"the",
"and",
"to",
"distributed",
"with",
"in",
"of"
],
[
"time",
"recurrent",
"series",
"dynamics",
"the",
"rnn",
"for",
"of",
"temporal",
"measurement"
],
[
"net",
"diffusion",
"recovery",
"communities",
"community",
"information",
"in",
"to",
"this",
"of"
],
[
"the",
"nodes",
"node",
"clustering",
"to",
"of",
"graph",
"graphs",
"and",
"we"
],
[
"search",
"vae",
"mobile",
"architecture",
"end",
"hardware",
"to",
"and",
"architectures",
"neural"
],
[
"implementations",
"extensions",
"updated",
"millions",
"base",
"minimal",
"entities",
"down",
"transforms",
"having"
],
[
"adversarial",
"training",
"to",
"in",
"the",
"attacks",
"of",
"and",
"we",
"is"
],
[
"to",
"and",
"the",
"of",
"is",
"channel",
"an",
"semantic",
"3d",
"for"
],
[
"semi",
"learning",
"of",
"supervised",
"the",
"we",
"and",
"random",
"features",
"ct"
],
[
"to",
"of",
"the",
"and",
"in",
"for",
"is",
"this",
"sharing",
"eeg"
],
[
"to",
"the",
"and",
"of",
"we",
"for",
"in",
"on",
"with",
"that"
],
[
"neural",
"to",
"and",
"network",
"the",
"networks",
"energy",
"convolutional",
"of",
"training"
],
[
"on",
"protein",
"generative",
"sequence",
"sequences",
"to",
"learning",
"methods",
"of",
"knowledge"
],
[
"learning",
"meta",
"reinforcement",
"games",
"the",
"policy",
"and",
"that",
"agent",
"action"
],
[
"and",
"learning",
"the",
"we",
"to",
"in",
"of",
"for",
"tasks",
"methods"
],
[
"10",
"confidence",
"100",
"cifar",
"to",
"unlabeled",
"that",
"on",
"we",
"only"
],
[
"of",
"fairness",
"we",
"learning",
"and",
"to",
"the",
"in",
"distance",
"metric"
],
[
"for",
"text",
"in",
"we",
"on",
"of",
"language",
"and",
"the",
"to"
],
[
"of",
"in",
"we",
"for",
"that",
"the",
"this",
"to",
"and",
"on"
],
[
"the",
"object",
"objects",
"and",
"uncertainty",
"estimation",
"driving",
"to",
"of",
"we"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"precision",
"on",
"of",
"and",
"that",
"the",
"in",
"to",
"models",
"learner"
],
[
"with",
"to",
"and",
"of",
"in",
"the",
"is",
"for",
"we",
"this"
],
[
"in",
"the",
"to",
"of",
"and",
"we",
"is",
"that",
"for",
"this"
],
[
"bandit",
"the",
"and",
"to",
"agents",
"agent",
"of",
"by",
"in",
"is"
],
[
"and",
"we",
"the",
"of",
"for",
"in",
"to",
"is",
"with",
"that"
],
[
"super",
"global",
"minimax",
"to",
"points",
"point",
"local",
"model",
"score",
"the"
],
[
"to",
"contrastive",
"our",
"image",
"the",
"method",
"data",
"fine",
"tasks",
"imagenet"
],
[
"of",
"in",
"to",
"the",
"and",
"is",
"that",
"we",
"model",
"by"
],
[
"learning",
"of",
"on",
"model",
"to",
"we",
"the",
"training",
"and",
"in"
],
[
"memory",
"the",
"of",
"term",
"and",
"network",
"to",
"flow",
"in",
"attention"
]
] | 13.227212 | all-MiniLM-L6-v2 | 0.4 | -0.058412 | 0.186957 | 0.626419 |
ArXiv ML Papers | LDA | 46 | 40 | [
[
"in",
"the",
"networks",
"network",
"of",
"neural",
"we",
"and",
"to",
"that"
],
[
"fairness",
"that",
"in",
"and",
"to",
"is",
"we",
"the",
"for",
"of"
],
[
"the",
"in",
"on",
"we",
"and",
"of",
"to",
"graph",
"our",
"with"
],
[
"learning",
"of",
"the",
"and",
"to",
"in",
"we",
"that",
"communication",
"agents"
],
[
"svm",
"tensor",
"machine",
"vector",
"overfitting",
"the",
"support",
"rank",
"an",
"one"
],
[
"to",
"learning",
"in",
"the",
"policy",
"and",
"of",
"reinforcement",
"we",
"is"
],
[
"of",
"and",
"the",
"to",
"in",
"is",
"we",
"for",
"that",
"on"
],
[
"the",
"in",
"of",
"we",
"is",
"to",
"and",
"that",
"with",
"for"
],
[
"images",
"on",
"of",
"to",
"the",
"and",
"we",
"that",
"in",
"with"
],
[
"the",
"points",
"local",
"traffic",
"with",
"driving",
"to",
"sharing",
"and",
"of"
],
[
"is",
"pooling",
"25",
"rnn",
"expression",
"the",
"in",
"recurrent",
"average",
"of"
],
[
"the",
"and",
"of",
"we",
"gradient",
"optimization",
"for",
"to",
"stochastic",
"convergence"
],
[
"to",
"objects",
"and",
"of",
"the",
"for",
"that",
"we",
"signals",
"sequences"
],
[
"text",
"vae",
"the",
"trees",
"generation",
"for",
"and",
"of",
"joint",
"that"
],
[
"the",
"and",
"data",
"model",
"gan",
"we",
"of",
"generative",
"methods",
"in"
],
[
"embeddings",
"3d",
"representations",
"segmentation",
"the",
"shape",
"for",
"learn",
"to",
"pipeline"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"to",
"attacks",
"adversarial",
"the",
"of",
"that",
"and",
"training",
"attack",
"we"
],
[
"and",
"the",
"to",
"in",
"we",
"carlo",
"of",
"monte",
"that",
"samples"
],
[
"video",
"feature",
"features",
"the",
"frame",
"in",
"fusion",
"of",
"module",
"this"
],
[
"of",
"to",
"the",
"and",
"we",
"on",
"in",
"that",
"for",
"our"
],
[
"and",
"of",
"to",
"for",
"the",
"in",
"this",
"we",
"that",
"is"
],
[
"processes",
"process",
"conditional",
"density",
"of",
"we",
"gaussian",
"the",
"on",
"in"
],
[
"languages",
"probabilistic",
"inference",
"algorithm",
"improve",
"impossible",
"substantial",
"program",
"make",
"programming"
],
[
"of",
"to",
"the",
"metric",
"data",
"in",
"learning",
"we",
"ensemble",
"with"
],
[
"the",
"and",
"learning",
"to",
"in",
"we",
"of",
"domain",
"on",
"is"
],
[
"extracted",
"extraction",
"co",
"classifier",
"regions",
"filter",
"image",
"filters",
"feature",
"by"
],
[
"to",
"the",
"deep",
"in",
"learning",
"we",
"of",
"for",
"and",
"speech"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"word",
"embedding",
"similarity",
"concepts",
"between",
"words",
"vectors",
"vector",
"measure",
"to"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"the",
"latent",
"to",
"manifold",
"variational",
"of",
"dimensional",
"and",
"we",
"variables"
],
[
"for",
"of",
"and",
"to",
"time",
"we",
"the",
"that",
"in",
"series"
],
[
"to",
"the",
"of",
"in",
"and",
"image",
"is",
"model",
"method",
"this"
],
[
"the",
"in",
"of",
"and",
"face",
"to",
"flow",
"we",
"for",
"on"
],
[
"with",
"and",
"of",
"on",
"in",
"the",
"for",
"to",
"classification",
"is"
],
[
"to",
"the",
"of",
"is",
"in",
"and",
"for",
"by",
"are",
"this"
],
[
"term",
"temporal",
"short",
"prediction",
"long",
"spatial",
"dynamics",
"and",
"view",
"demand"
],
[
"the",
"is",
"of",
"net",
"quantum",
"and",
"for",
"to",
"estimator",
"treatment"
]
] | 13.765348 | all-MiniLM-L6-v2 | 0.3825 | -0.072976 | 0.192218 | 0.644522 |
ArXiv ML Papers | LDA | 43 | 50 | [
[
"is",
"of",
"for",
"distributed",
"the",
"communication",
"to",
"and",
"data",
"as"
],
[
"cifar",
"transformation",
"the",
"10",
"transformations",
"relu",
"activation",
"neurons",
"100",
"of"
],
[
"exploration",
"bandit",
"strategies",
"depth",
"regret",
"scaling",
"combinatorial",
"and",
"strategy",
"in"
],
[
"quantum",
"classification",
"traffic",
"the",
"data",
"in",
"to",
"of",
"and",
"is"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"adversarial",
"we",
"the",
"in",
"of",
"to",
"and",
"attacks",
"that",
"training"
],
[
"of",
"we",
"the",
"to",
"and",
"in",
"that",
"is",
"for",
"this"
],
[
"semi",
"unsupervised",
"supervised",
"learning",
"the",
"self",
"data",
"and",
"in",
"labeled"
],
[
"of",
"ml",
"and",
"that",
"the",
"to",
"in",
"can",
"be",
"is"
],
[
"the",
"and",
"to",
"of",
"with",
"is",
"network",
"training",
"neural",
"architecture"
],
[
"to",
"tensor",
"of",
"agent",
"agents",
"the",
"we",
"online",
"and",
"regret"
],
[
"to",
"in",
"the",
"is",
"of",
"and",
"for",
"based",
"are",
"algorithms"
],
[
"the",
"of",
"to",
"and",
"in",
"we",
"learning",
"that",
"for",
"tasks"
],
[
"and",
"of",
"gradient",
"the",
"we",
"in",
"to",
"for",
"optimization",
"on"
],
[
"is",
"to",
"for",
"and",
"the",
"method",
"in",
"of",
"model",
"an"
],
[
"and",
"to",
"for",
"in",
"federated",
"the",
"of",
"is",
"that",
"on"
],
[
"of",
"the",
"and",
"for",
"to",
"is",
"this",
"we",
"in",
"on"
],
[
"items",
"recommendation",
"user",
"right",
"left",
"frac",
"recommender",
"item",
"systems",
"varepsilon"
],
[
"fairness",
"in",
"we",
"and",
"to",
"the",
"bias",
"that",
"of",
"on"
],
[
"and",
"privacy",
"private",
"learning",
"data",
"to",
"the",
"sensitive",
"of",
"we"
],
[
"the",
"of",
"and",
"to",
"is",
"in",
"we",
"on",
"this",
"that"
],
[
"moving",
"selection",
"head",
"of",
"smart",
"motion",
"sensing",
"for",
"and",
"free"
],
[
"questions",
"question",
"and",
"our",
"that",
"answer",
"given",
"materials",
"framework",
"science"
],
[
"kernels",
"imitation",
"of",
"wireless",
"in",
"mixed",
"imputation",
"compositional",
"conditions",
"kernel"
],
[
"for",
"we",
"and",
"the",
"of",
"in",
"to",
"with",
"algorithm",
"is"
],
[
"the",
"to",
"event",
"and",
"of",
"in",
"cnn",
"events",
"location",
"based"
],
[
"of",
"the",
"to",
"for",
"that",
"and",
"in",
"with",
"performance",
"models"
],
[
"and",
"the",
"text",
"of",
"language",
"in",
"for",
"natural",
"translation",
"generation"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"for",
"to",
"search",
"of",
"we",
"from",
"sparse",
"the",
"that",
"and"
],
[
"student",
"in",
"the",
"and",
"teacher",
"based",
"flow",
"to",
"we",
"sequences"
],
[
"bayesian",
"of",
"the",
"we",
"processes",
"and",
"posterior",
"model",
"to",
"with"
],
[
"in",
"and",
"we",
"of",
"for",
"the",
"classifier",
"classification",
"label",
"that"
],
[
"to",
"for",
"of",
"resolution",
"graphs",
"in",
"the",
"video",
"graph",
"and"
],
[
"we",
"data",
"that",
"in",
"the",
"for",
"of",
"this",
"to",
"and"
],
[
"the",
"and",
"we",
"of",
"for",
"models",
"to",
"in",
"on",
"inference"
],
[
"the",
"and",
"object",
"in",
"for",
"by",
"we",
"to",
"of",
"objects"
],
[
"recommendations",
"fair",
"and",
"of",
"group",
"algorithms",
"our",
"groups",
"the",
"to"
],
[
"of",
"that",
"to",
"in",
"and",
"the",
"we",
"is",
"learning",
"on"
],
[
"of",
"to",
"and",
"distance",
"in",
"the",
"for",
"we",
"is",
"this"
],
[
"we",
"to",
"and",
"in",
"of",
"the",
"is",
"data",
"for",
"that"
],
[
"document",
"word",
"topic",
"semantic",
"representations",
"level",
"words",
"documents",
"sentences",
"topics"
],
[
"the",
"and",
"of",
"in",
"to",
"we",
"machine",
"speech",
"channel",
"on"
],
[
"energy",
"systems",
"of",
"to",
"sharing",
"demand",
"devices",
"the",
"and",
"heterogeneous"
],
[
"policies",
"rl",
"reinforcement",
"epsilon",
"in",
"learning",
"the",
"of",
"policy",
"optimal"
],
[
"deep",
"on",
"we",
"and",
"of",
"to",
"the",
"in",
"learning",
"with"
],
[
"of",
"learning",
"that",
"the",
"to",
"training",
"and",
"in",
"from",
"we"
],
[
"and",
"for",
"to",
"of",
"the",
"in",
"on",
"is",
"model",
"performance"
],
[
"surrogate",
"models",
"fidelity",
"off",
"trade",
"to",
"high",
"and",
"programs",
"sampling"
],
[
"and",
"the",
"graph",
"of",
"signals",
"on",
"speech",
"in",
"to",
"network"
]
] | 23.874444 | all-MiniLM-L6-v2 | 0.364 | -0.051004 | 0.189744 | 0.61902 |
ArXiv ML Papers | LDA | 44 | 50 | [
[
"image",
"generative",
"gan",
"the",
"to",
"of",
"generator",
"images",
"gans",
"and"
],
[
"of",
"and",
"in",
"the",
"to",
"we",
"for",
"domain",
"model",
"on"
],
[
"of",
"in",
"to",
"and",
"that",
"the",
"we",
"this",
"are",
"on"
],
[
"search",
"the",
"architecture",
"and",
"of",
"shift",
"to",
"nas",
"parameter",
"ranking"
],
[
"adversarial",
"attacks",
"training",
"the",
"robustness",
"attack",
"to",
"we",
"that",
"robust"
],
[
"processes",
"kernels",
"mixed",
"gaussian",
"are",
"compositional",
"recursive",
"gps",
"stochastic",
"the"
],
[
"to",
"the",
"and",
"of",
"we",
"is",
"in",
"that",
"model",
"on"
],
[
"to",
"of",
"and",
"the",
"can",
"for",
"in",
"machine",
"be",
"we"
],
[
"to",
"learning",
"the",
"and",
"of",
"in",
"we",
"reinforcement",
"agent",
"that"
],
[
"for",
"term",
"time",
"net",
"we",
"the",
"and",
"in",
"to",
"of"
],
[
"the",
"of",
"on",
"in",
"and",
"we",
"to",
"metric",
"learning",
"that"
],
[
"of",
"people",
"and",
"for",
"sensing",
"user",
"text",
"users",
"head",
"study"
],
[
"systems",
"system",
"to",
"learning",
"continuous",
"production",
"rules",
"asynchronous",
"logic",
"of"
],
[
"data",
"the",
"we",
"to",
"of",
"learning",
"and",
"methods",
"in",
"is"
],
[
"and",
"to",
"in",
"of",
"we",
"the",
"that",
"on",
"for",
"is"
],
[
"xgboost",
"in",
"the",
"dense",
"using",
"agents",
"for",
"single",
"different",
"of"
],
[
"for",
"to",
"the",
"and",
"learning",
"in",
"of",
"we",
"machine",
"this"
],
[
"for",
"with",
"layer",
"the",
"of",
"to",
"cnn",
"and",
"based",
"in"
],
[
"the",
"to",
"and",
"of",
"we",
"for",
"in",
"algorithm",
"is",
"convergence"
],
[
"and",
"we",
"of",
"the",
"that",
"learning",
"in",
"to",
"for",
"is"
],
[
"that",
"the",
"for",
"in",
"and",
"is",
"of",
"data",
"to",
"we"
],
[
"ensemble",
"to",
"and",
"of",
"on",
"the",
"in",
"memory",
"is",
"we"
],
[
"the",
"of",
"and",
"in",
"model",
"policy",
"we",
"to",
"speech",
"for"
],
[
"optimization",
"gradient",
"method",
"and",
"we",
"the",
"stochastic",
"epsilon",
"order",
"of"
],
[
"in",
"selection",
"and",
"features",
"classification",
"feature",
"the",
"dataset",
"we",
"data"
],
[
"with",
"this",
"in",
"we",
"of",
"the",
"and",
"for",
"to",
"is"
],
[
"markov",
"carlo",
"monte",
"chain",
"mcmc",
"sampling",
"distribution",
"the",
"parallel",
"we"
],
[
"contrastive",
"self",
"augmentation",
"distributed",
"data",
"across",
"10",
"supervised",
"locations",
"to"
],
[
"runtime",
"generalize",
"al",
"2018",
"strategy",
"samples",
"nearly",
"runs",
"et",
"action"
],
[
"network",
"of",
"to",
"the",
"we",
"for",
"convolutional",
"layers",
"and",
"with"
],
[
"in",
"we",
"is",
"for",
"of",
"to",
"that",
"and",
"the",
"algorithm"
],
[
"low",
"tensor",
"in",
"rank",
"on",
"pruning",
"to",
"and",
"tensors",
"of"
],
[
"and",
"in",
"of",
"data",
"the",
"we",
"to",
"that",
"on",
"training"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"of",
"the",
"for",
"sensitivity",
"hardware",
"that",
"to",
"neural",
"network",
"in"
],
[
"and",
"of",
"data",
"the",
"to",
"system",
"in",
"is",
"systems",
"for"
],
[
"node",
"of",
"and",
"on",
"graph",
"graphs",
"nodes",
"the",
"we",
"to"
],
[
"for",
"is",
"in",
"to",
"that",
"of",
"and",
"the",
"we",
"as"
],
[
"confidence",
"delta",
"the",
"and",
"to",
"costs",
"lower",
"left",
"of",
"right"
],
[
"and",
"the",
"to",
"sequence",
"of",
"mean",
"is",
"are",
"in",
"for"
],
[
"and",
"of",
"the",
"map",
"to",
"profiles",
"network",
"interpretation",
"forgetting",
"generalized"
],
[
"that",
"in",
"is",
"the",
"to",
"of",
"and",
"deep",
"network",
"segmentation"
],
[
"games",
"of",
"classical",
"that",
"game",
"learning",
"cloud",
"we",
"for",
"the"
],
[
"the",
"of",
"to",
"and",
"in",
"detection",
"on",
"is",
"for",
"we"
],
[
"in",
"series",
"data",
"to",
"for",
"signals",
"time",
"of",
"the",
"and"
],
[
"data",
"the",
"for",
"and",
"is",
"energy",
"sensor",
"of",
"dl",
"in"
],
[
"to",
"network",
"neural",
"networks",
"the",
"of",
"in",
"and",
"that",
"we"
],
[
"quantum",
"the",
"of",
"big",
"and",
"data",
"to",
"for",
"in",
"learning"
],
[
"model",
"and",
"bert",
"the",
"event",
"we",
"to",
"of",
"based",
"in"
],
[
"in",
"and",
"that",
"we",
"the",
"noise",
"flow",
"to",
"dimensional",
"high"
]
] | 14.476796 | all-MiniLM-L6-v2 | 0.356 | -0.035463 | 0.186418 | 0.596312 |
ArXiv ML Papers | LDA | 45 | 50 | [
[
"and",
"of",
"the",
"matrix",
"is",
"we",
"that",
"for",
"in",
"on"
],
[
"the",
"to",
"learning",
"in",
"and",
"policy",
"we",
"reinforcement",
"that",
"of"
],
[
"classification",
"in",
"the",
"is",
"learning",
"and",
"classifier",
"of",
"label",
"domain"
],
[
"and",
"the",
"spatial",
"sharing",
"to",
"grained",
"demand",
"fine",
"reconstruction",
"of"
],
[
"of",
"the",
"segmentation",
"and",
"to",
"for",
"neural",
"as",
"image",
"in"
],
[
"inference",
"based",
"model",
"speech",
"recognition",
"free",
"performance",
"ratio",
"algorithm",
"using"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"data",
"with",
"for",
"of",
"the",
"in",
"and",
"to",
"is",
"we"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"the",
"for",
"of",
"and",
"is",
"regression",
"to",
"with",
"data",
"in"
],
[
"the",
"rnn",
"recurrent",
"dynamics",
"of",
"sequence",
"temporal",
"time",
"lstm",
"system"
],
[
"groups",
"outcomes",
"interventions",
"machine",
"that",
"across",
"fair",
"ml",
"fairness",
"we"
],
[
"the",
"to",
"clustering",
"we",
"and",
"of",
"flow",
"for",
"with",
"network"
],
[
"post",
"start",
"means",
"outcome",
"lack",
"technique",
"knowledge",
"effects",
"hoc",
"interpretability"
],
[
"explanations",
"decision",
"schemes",
"explain",
"aggregation",
"aggregated",
"aggregate",
"methods",
"explaining",
"explanation"
],
[
"the",
"to",
"of",
"and",
"is",
"we",
"on",
"in",
"search",
"our"
],
[
"user",
"to",
"the",
"and",
"items",
"users",
"we",
"of",
"recommendation",
"in"
],
[
"of",
"active",
"similarity",
"the",
"distance",
"learning",
"metric",
"to",
"data",
"variables"
],
[
"the",
"computing",
"for",
"of",
"to",
"and",
"in",
"we",
"is",
"data"
],
[
"question",
"the",
"and",
"research",
"in",
"questions",
"of",
"to",
"for",
"open"
],
[
"networks",
"energy",
"to",
"for",
"and",
"we",
"of",
"privacy",
"the",
"traffic"
],
[
"devices",
"sequence",
"learning",
"protein",
"sequences",
"device",
"power",
"edge",
"on",
"to"
],
[
"bias",
"the",
"games",
"error",
"that",
"we",
"variance",
"in",
"and",
"of"
],
[
"of",
"the",
"and",
"quantum",
"in",
"for",
"that",
"we",
"noise",
"to"
],
[
"the",
"points",
"point",
"local",
"global",
"to",
"signals",
"of",
"minimax",
"equations"
],
[
"the",
"to",
"and",
"of",
"learning",
"in",
"geometric",
"translation",
"we",
"view"
],
[
"of",
"graph",
"to",
"node",
"we",
"and",
"graphs",
"in",
"the",
"on"
],
[
"the",
"we",
"and",
"of",
"to",
"in",
"that",
"for",
"on",
"this"
],
[
"image",
"of",
"and",
"images",
"gan",
"the",
"we",
"to",
"generative",
"generation"
],
[
"transmission",
"wireless",
"anomaly",
"coding",
"channel",
"codes",
"detection",
"anomalies",
"that",
"the"
],
[
"the",
"information",
"to",
"in",
"latent",
"variational",
"of",
"likelihood",
"autoencoders",
"and"
],
[
"the",
"of",
"clustering",
"for",
"to",
"we",
"in",
"and",
"that",
"with"
],
[
"is",
"to",
"the",
"that",
"we",
"in",
"and",
"of",
"for",
"this"
],
[
"of",
"is",
"objects",
"to",
"feedback",
"online",
"the",
"algorithms",
"by",
"that"
],
[
"for",
"of",
"to",
"the",
"we",
"and",
"in",
"is",
"that",
"optimization"
],
[
"covid",
"19",
"amenable",
"sample",
"spectrum",
"our",
"evidence",
"provable",
"analysis",
"we"
],
[
"scene",
"we",
"tensor",
"of",
"the",
"low",
"rank",
"approximation",
"for",
"and"
],
[
"the",
"in",
"we",
"to",
"model",
"and",
"of",
"is",
"this",
"that"
],
[
"learning",
"the",
"and",
"of",
"to",
"for",
"in",
"model",
"we",
"with"
],
[
"of",
"the",
"and",
"prediction",
"in",
"time",
"long",
"short",
"traffic",
"term"
],
[
"to",
"for",
"we",
"and",
"in",
"on",
"data",
"is",
"of",
"the"
],
[
"off",
"fidelity",
"trade",
"high",
"accuracy",
"models",
"to",
"cost",
"surrogate",
"of"
],
[
"robust",
"concepts",
"to",
"and",
"that",
"the",
"cloud",
"learning",
"meta",
"we"
],
[
"to",
"the",
"we",
"in",
"and",
"learning",
"on",
"of",
"training",
"data"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"the",
"in",
"and",
"to",
"is",
"on",
"of",
"for",
"this",
"with"
],
[
"the",
"depth",
"manifold",
"and",
"we",
"of",
"to",
"that",
"subspace",
"shift"
],
[
"of",
"and",
"networks",
"network",
"to",
"neural",
"the",
"with",
"we",
"in"
],
[
"and",
"adversarial",
"the",
"to",
"attacks",
"we",
"of",
"training",
"detection",
"in"
],
[
"the",
"to",
"and",
"data",
"is",
"of",
"in",
"for",
"that",
"on"
]
] | 14.255966 | all-MiniLM-L6-v2 | 0.39 | -0.061498 | 0.18372 | 0.637337 |
ArXiv ML Papers | LDA | 46 | 50 | [
[
"the",
"of",
"to",
"in",
"and",
"on",
"is",
"we",
"network",
"model"
],
[
"and",
"the",
"of",
"we",
"is",
"in",
"to",
"for",
"with",
"that"
],
[
"the",
"and",
"in",
"to",
"tensor",
"traffic",
"of",
"net",
"on",
"for"
],
[
"inference",
"we",
"and",
"to",
"learning",
"the",
"of",
"for",
"in",
"variational"
],
[
"to",
"regression",
"protein",
"knowledge",
"learning",
"in",
"this",
"sequences",
"for",
"sequence"
],
[
"of",
"in",
"and",
"the",
"to",
"channel",
"as",
"is",
"this",
"for"
],
[
"the",
"to",
"and",
"of",
"in",
"is",
"we",
"for",
"that",
"data"
],
[
"the",
"of",
"to",
"we",
"that",
"in",
"is",
"and",
"with",
"for"
],
[
"of",
"to",
"we",
"the",
"and",
"on",
"our",
"that",
"in",
"object"
],
[
"actions",
"in",
"local",
"minimax",
"and",
"of",
"to",
"driving",
"action",
"the"
],
[
"uncertainty",
"in",
"not",
"to",
"the",
"predictions",
"decision",
"confidence",
"want",
"which"
],
[
"for",
"gradient",
"optimization",
"policy",
"of",
"the",
"we",
"and",
"stochastic",
"with"
],
[
"to",
"detection",
"the",
"of",
"objects",
"in",
"for",
"transformation",
"we",
"cnn"
],
[
"metric",
"distance",
"the",
"we",
"learning",
"to",
"of",
"from",
"trees",
"and"
],
[
"of",
"and",
"data",
"series",
"we",
"model",
"time",
"the",
"to",
"forecasting"
],
[
"representation",
"node",
"of",
"graphs",
"embedding",
"the",
"graph",
"nodes",
"to",
"network"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"in",
"is",
"that",
"to",
"the",
"and",
"of",
"with",
"we",
"problem"
],
[
"for",
"in",
"the",
"we",
"of",
"to",
"and",
"energy",
"cost",
"monte"
],
[
"features",
"video",
"each",
"fusion",
"as",
"frame",
"frames",
"this",
"feature",
"uses"
],
[
"and",
"to",
"we",
"of",
"the",
"in",
"that",
"for",
"on",
"language"
],
[
"for",
"to",
"and",
"in",
"the",
"of",
"we",
"this",
"that",
"is"
],
[
"of",
"are",
"in",
"region",
"regions",
"the",
"reports",
"this",
"with",
"to"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"data",
"of",
"the",
"kernel",
"in",
"and",
"we",
"to",
"on",
"is"
],
[
"in",
"domain",
"and",
"the",
"to",
"of",
"transfer",
"target",
"learning",
"knowledge"
],
[
"and",
"demand",
"bottleneck",
"distributed",
"each",
"in",
"communication",
"this",
"popular",
"data"
],
[
"the",
"and",
"privacy",
"to",
"of",
"user",
"users",
"we",
"on",
"private"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"in",
"measure",
"words",
"between",
"similarity",
"vectors",
"word",
"to",
"concepts",
"this"
],
[
"processes",
"our",
"complexity",
"gaussian",
"sample",
"approximation",
"full",
"we",
"latent",
"with"
],
[
"and",
"rl",
"agent",
"the",
"learning",
"agents",
"to",
"reinforcement",
"environment",
"we"
],
[
"adversarial",
"the",
"to",
"we",
"training",
"and",
"that",
"of",
"attacks",
"on"
],
[
"image",
"of",
"to",
"the",
"and",
"in",
"we",
"for",
"images",
"on"
],
[
"on",
"to",
"the",
"in",
"method",
"ensemble",
"prediction",
"of",
"and",
"with"
],
[
"of",
"the",
"and",
"to",
"in",
"for",
"from",
"with",
"that",
"is"
],
[
"we",
"this",
"of",
"to",
"in",
"the",
"for",
"and",
"model",
"is"
],
[
"term",
"short",
"prediction",
"long",
"temporal",
"dynamics",
"spatial",
"view",
"and",
"system"
],
[
"for",
"to",
"the",
"learning",
"risk",
"and",
"of",
"is",
"in",
"prediction"
],
[
"resolution",
"super",
"types",
"type",
"identity",
"of",
"the",
"and",
"to",
"task"
],
[
"algorithm",
"of",
"to",
"we",
"the",
"that",
"is",
"in",
"for",
"and"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"personalized",
"perspective",
"performed",
"performing",
"performs",
"period",
"periodic",
"periods",
"person",
"personal"
],
[
"entropy",
"function",
"loss",
"shift",
"functions",
"margin",
"cross",
"networks",
"and",
"neural"
],
[
"of",
"the",
"to",
"is",
"in",
"and",
"that",
"this",
"we",
"by"
],
[
"in",
"the",
"and",
"to",
"of",
"memory",
"with",
"on",
"networks",
"neural"
],
[
"the",
"of",
"and",
"to",
"is",
"we",
"by",
"data",
"this",
"in"
],
[
"in",
"to",
"supervised",
"representations",
"unsupervised",
"of",
"the",
"we",
"training",
"be"
],
[
"features",
"feature",
"learning",
"deep",
"games",
"methods",
"to",
"semi",
"for",
"and"
]
] | 14.030783 | all-MiniLM-L6-v2 | 0.33 | -0.072721 | 0.191163 | 0.638094 |
ArXiv ML Papers | Top2Vec | 43 | 10 | [
[
"cnns",
"cnn",
"adversary",
"classifiers",
"supervised",
"adversarial",
"adversarially",
"softmax",
"classifier",
"imagenet"
],
[
"learnt",
"learns",
"ai",
"bandit",
"bandits",
"learnable",
"reinforcement",
"learning",
"planning",
"softmax"
],
[
"rnns",
"lstm",
"softmax",
"corpus",
"learns",
"autoencoder",
"autoencoders",
"embeddings",
"supervised",
"cnn"
],
[
"softmax",
"forecasts",
"predicts",
"predict",
"prediction",
"forecasting",
"predicting",
"forecast",
"classifiers",
"rnns"
],
[
"regularization",
"optimizer",
"supervised",
"sparse",
"lasso",
"softmax",
"classifiers",
"regularized",
"classification",
"boosting"
],
[
"nodes",
"graphs",
"graph",
"networks",
"cnn",
"cnns",
"vertex",
"embeddings",
"softmax",
"supervised"
],
[
"supervised",
"imagenet",
"softmax",
"classifying",
"cnn",
"classifiers",
"cnns",
"classification",
"autoencoders",
"neural"
],
[
"backpropagation",
"imagenet",
"softmax",
"cnn",
"rnns",
"cnns",
"autoencoders",
"neural",
"networks",
"learns"
],
[
"gans",
"autoencoders",
"imagenet",
"cnn",
"cnns",
"adversarial",
"convolutions",
"autoencoder",
"adversarially",
"recognition"
],
[
"supervised",
"softmax",
"generative",
"autoencoders",
"rnns",
"probabilistic",
"learns",
"backpropagation",
"models",
"priors"
]
] | 656.29102 | all-MiniLM-L6-v2 | 0.54 | -0.278403 | 0.14962 | 0.846283 |
ArXiv ML Papers | Top2Vec | 44 | 10 | [
[
"supervised",
"adversarial",
"adversarially",
"classifiers",
"classifier",
"softmax",
"classification",
"classifying",
"adversary",
"cnns"
],
[
"ai",
"reinforcement",
"bandit",
"planning",
"learning",
"learns",
"learnt",
"bandits",
"learnable",
"softmax"
],
[
"softmax",
"autoencoders",
"learns",
"cnns",
"cnn",
"neural",
"networks",
"imagenet",
"rnns",
"backpropagation"
],
[
"supervised",
"softmax",
"embeddings",
"graphs",
"networks",
"cnn",
"cnns",
"rnns",
"nodes",
"embedding"
],
[
"predicting",
"forecasts",
"forecasting",
"forecast",
"prediction",
"softmax",
"rnns",
"classifiers",
"predicts",
"predict"
],
[
"autoencoder",
"softmax",
"corpus",
"learns",
"rnns",
"cnn",
"neural",
"autoencoders",
"lstm",
"cnns"
],
[
"lasso",
"sparse",
"regularization",
"minimization",
"optimizer",
"softmax",
"optimizing",
"minimize",
"optimization",
"regularized"
],
[
"models",
"supervised",
"autoencoders",
"softmax",
"generative",
"priors",
"learning",
"rnns",
"probabilistic",
"learns"
],
[
"softmax",
"cnns",
"cnn",
"autoencoders",
"supervised",
"recognition",
"imagenet",
"neural",
"classifiers",
"classifying"
],
[
"autoencoders",
"supervised",
"cnn",
"adversarial",
"cnns",
"imagenet",
"gans",
"adversarially",
"autoencoder",
"convolutions"
]
] | 673.252285 | all-MiniLM-L6-v2 | 0.56 | -0.26459 | 0.160135 | 0.845661 |
ArXiv ML Papers | Top2Vec | 45 | 10 | [
[
"learning",
"learns",
"learnt",
"reinforcement",
"bandit",
"bandits",
"ai",
"planning",
"learnable",
"softmax"
],
[
"adversarial",
"softmax",
"adversarially",
"classifier",
"classifiers",
"supervised",
"adversary",
"cnns",
"classification",
"classifying"
],
[
"rnns",
"lstm",
"learns",
"autoencoders",
"softmax",
"corpus",
"autoencoder",
"supervised",
"classifiers",
"cnn"
],
[
"backpropagation",
"neural",
"cnns",
"cnn",
"imagenet",
"softmax",
"rnns",
"autoencoders",
"networks",
"learns"
],
[
"predict",
"forecasts",
"forecasting",
"forecast",
"softmax",
"predicts",
"rnns",
"prediction",
"predicting",
"lstm"
],
[
"networks",
"nodes",
"graphs",
"graph",
"cnn",
"cnns",
"embeddings",
"softmax",
"vertex",
"supervised"
],
[
"softmax",
"autoencoders",
"supervised",
"models",
"rnns",
"generative",
"backpropagation",
"probabilistic",
"priors",
"learns"
],
[
"neural",
"classifying",
"autoencoders",
"softmax",
"classifiers",
"imagenet",
"supervised",
"cnns",
"classification",
"cnn"
],
[
"cnn",
"autoencoders",
"generative",
"adversarially",
"adversarial",
"gans",
"cnns",
"imagenet",
"autoencoder",
"convolutions"
],
[
"softmax",
"benchmarks",
"minimize",
"minimization",
"regularized",
"regularization",
"optimizer",
"lasso",
"sparse",
"optimizing"
]
] | 584.320705 | all-MiniLM-L6-v2 | 0.56 | -0.273268 | 0.174848 | 0.846271 |
ArXiv ML Papers | Top2Vec | 46 | 10 | [
[
"learning",
"ai",
"planning",
"reinforcement",
"bandit",
"learnt",
"learns",
"bandits",
"learnable",
"softmax"
],
[
"adversary",
"softmax",
"adversarial",
"adversarially",
"classifiers",
"cnns",
"supervised",
"cnn",
"classifier",
"imagenet"
],
[
"softmax",
"autoencoder",
"lstm",
"supervised",
"rnns",
"embeddings",
"learns",
"corpus",
"autoencoders",
"cnn"
],
[
"forecasts",
"forecasting",
"forecast",
"prediction",
"predict",
"predicting",
"predicts",
"softmax",
"classifiers",
"rnns"
],
[
"networks",
"graphs",
"nodes",
"graph",
"cnn",
"softmax",
"cnns",
"supervised",
"vertex",
"embeddings"
],
[
"classification",
"classifying",
"imagenet",
"autoencoders",
"cnn",
"classifiers",
"cnns",
"supervised",
"softmax",
"classify"
],
[
"regularization",
"lasso",
"softmax",
"supervised",
"regularized",
"optimizer",
"classifiers",
"sparse",
"boosting",
"minimization"
],
[
"rnns",
"softmax",
"neural",
"imagenet",
"backpropagation",
"autoencoders",
"cnn",
"cnns",
"networks",
"learns"
],
[
"adversarially",
"cnn",
"imagenet",
"autoencoders",
"cnns",
"gans",
"adversarial",
"generative",
"supervised",
"autoencoder"
],
[
"autoencoders",
"learns",
"rnns",
"generative",
"softmax",
"models",
"supervised",
"backpropagation",
"priors",
"autoencoder"
]
] | 634.524863 | all-MiniLM-L6-v2 | 0.53 | -0.269991 | 0.153078 | 0.853081 |
ArXiv ML Papers | Top2Vec | 43 | 20 | [
[
"adversarially",
"classifiers",
"discrimination",
"bias",
"biases",
"fairness",
"supervised",
"adversarial",
"discriminate",
"classifier"
],
[
"softmax",
"distributed",
"learns",
"federated",
"adversarially",
"learnt",
"learning",
"supervised",
"rnns",
"rnn"
],
[
"adversary",
"security",
"private",
"adversarial",
"softmax",
"regularization",
"classifiers",
"randomized",
"adversarially",
"privacy"
],
[
"adversarially",
"adversarial",
"cnn",
"cnns",
"classifiers",
"gans",
"softmax",
"autoencoders",
"adversary",
"imagenet"
],
[
"learnt",
"learns",
"ai",
"bandit",
"bandits",
"learnable",
"reinforcement",
"learning",
"planning",
"softmax"
],
[
"backpropagation",
"rnns",
"learns",
"softmax",
"neural",
"modeling",
"models",
"autoencoders",
"rnn",
"learning"
],
[
"personalized",
"factorization",
"recommender",
"recommendation",
"softmax",
"recommendations",
"embeddings",
"retrieval",
"supervised",
"ranking"
],
[
"lstm",
"corpus",
"learns",
"cnn",
"autoencoders",
"autoencoder",
"rnns",
"softmax",
"neural",
"cnns"
],
[
"neural",
"cnns",
"softmax",
"backpropagation",
"networks",
"regularization",
"rnns",
"imagenet",
"autoencoders",
"cnn"
],
[
"softmax",
"forecasts",
"predicts",
"predict",
"prediction",
"forecasting",
"predicting",
"forecast",
"classifiers",
"rnns"
],
[
"classify",
"classifying",
"neural",
"classification",
"classifier",
"softmax",
"lstm",
"classifiers",
"autoencoders",
"recognition"
],
[
"classifiers",
"classification",
"supervised",
"lasso",
"sparse",
"softmax",
"classifying",
"regularization",
"svm",
"regularized"
],
[
"nodes",
"graphs",
"graph",
"networks",
"cnn",
"cnns",
"vertex",
"embeddings",
"softmax",
"supervised"
],
[
"supervised",
"predicting",
"dataset",
"datasets",
"classifying",
"classify",
"classifiers",
"classifier",
"classification",
"softmax"
],
[
"gan",
"gans",
"adversarial",
"generative",
"autoencoders",
"adversarially",
"cnn",
"imagenet",
"autoencoder",
"cnns"
],
[
"imagenet",
"cnn",
"cnns",
"supervised",
"softmax",
"recognition",
"autoencoders",
"neural",
"segmentation",
"recognizing"
],
[
"bottleneck",
"benchmarks",
"autoencoder",
"networks",
"rnns",
"imagenet",
"autoencoders",
"cnns",
"cnn",
"optimized"
],
[
"convolutions",
"autoencoders",
"recognizing",
"supervised",
"imagenet",
"cnns",
"recognition",
"cnn",
"convolutional",
"softmax"
],
[
"supervised",
"softmax",
"generative",
"autoencoders",
"rnns",
"probabilistic",
"learns",
"backpropagation",
"models",
"priors"
],
[
"optimization",
"optimizer",
"regularization",
"lasso",
"minimize",
"minimization",
"optimizing",
"optimize",
"optimized",
"optimal"
]
] | 608.984458 | all-MiniLM-L6-v2 | 0.45 | -0.266672 | 0.173274 | 0.85077 |
ArXiv ML Papers | Top2Vec | 44 | 20 | [
[
"distributed",
"softmax",
"federated",
"learnt",
"learns",
"learning",
"supervised",
"adversarially",
"rnns",
"rnn"
],
[
"classifiers",
"adversary",
"cnns",
"cnn",
"attacks",
"adversarial",
"adversarially",
"softmax",
"exploiting",
"adversaries"
],
[
"fairness",
"biases",
"classifiers",
"classifier",
"adversarial",
"discrimination",
"discriminate",
"adversarially",
"supervised",
"bias"
],
[
"ai",
"reinforcement",
"bandit",
"planning",
"learning",
"learns",
"learnt",
"bandits",
"learnable",
"softmax"
],
[
"backpropagation",
"rnns",
"softmax",
"neural",
"autoencoders",
"learns",
"models",
"learning",
"modeling",
"rnn"
],
[
"softmax",
"neural",
"classifying",
"classify",
"autoencoders",
"classifiers",
"classifier",
"classification",
"supervised",
"lstm"
],
[
"graphs",
"softmax",
"supervised",
"networks",
"cnn",
"nodes",
"graph",
"cnns",
"embeddings",
"rnns"
],
[
"cnns",
"cnn",
"imagenet",
"softmax",
"regularization",
"neural",
"networks",
"backpropagation",
"autoencoders",
"rnns"
],
[
"predicting",
"forecasts",
"forecasting",
"forecast",
"prediction",
"softmax",
"rnns",
"classifiers",
"predicts",
"predict"
],
[
"embeddings",
"recommender",
"recommendations",
"recommendation",
"supervised",
"softmax",
"factorization",
"personalized",
"ranking",
"retrieval"
],
[
"autoencoder",
"softmax",
"corpus",
"learns",
"rnns",
"cnn",
"neural",
"autoencoders",
"lstm",
"cnns"
],
[
"classifier",
"classifying",
"boosting",
"supervised",
"classify",
"ensemble",
"classification",
"classifiers",
"ensembles",
"softmax"
],
[
"classification",
"metrics",
"classifiers",
"supervised",
"softmax",
"metric",
"similarity",
"classifying",
"classifier",
"regularization"
],
[
"optimizer",
"regularization",
"sparse",
"lasso",
"regularized",
"clustering",
"tensors",
"softmax",
"minimization",
"algorithms"
],
[
"autoencoders",
"imagenet",
"benchmarks",
"bottleneck",
"cnn",
"rnns",
"cnns",
"networks",
"neural",
"optimized"
],
[
"imagenet",
"generative",
"cnn",
"autoencoder",
"autoencoders",
"gans",
"adversarially",
"adversarial",
"gan",
"cnns"
],
[
"lasso",
"optimizer",
"optimize",
"optimization",
"minimization",
"minimize",
"regularization",
"optimizing",
"optimized",
"optimizes"
],
[
"models",
"supervised",
"autoencoders",
"softmax",
"generative",
"priors",
"learning",
"rnns",
"probabilistic",
"learns"
],
[
"imagenet",
"cnn",
"cnns",
"recognition",
"softmax",
"supervised",
"autoencoders",
"neural",
"segmentation",
"recognizing"
],
[
"recognizing",
"supervised",
"convolutions",
"imagenet",
"cnns",
"recognition",
"cnn",
"autoencoders",
"convolutional",
"classifiers"
]
] | 535.264348 | all-MiniLM-L6-v2 | 0.47 | -0.253657 | 0.174505 | 0.844253 |
ArXiv ML Papers | Top2Vec | 45 | 20 | [
[
"learning",
"learns",
"learnt",
"reinforcement",
"bandit",
"bandits",
"ai",
"planning",
"learnable",
"softmax"
],
[
"softmax",
"distributed",
"learns",
"federated",
"adversarially",
"learnt",
"learning",
"supervised",
"rnns",
"rnn"
],
[
"adversary",
"adversarial",
"adversarially",
"cnns",
"classifiers",
"softmax",
"cnn",
"exploiting",
"attacks",
"adversaries"
],
[
"biases",
"bias",
"fairness",
"adversarially",
"classifiers",
"adversarial",
"supervised",
"discrimination",
"discriminate",
"classifier"
],
[
"rnns",
"lstm",
"learns",
"autoencoders",
"softmax",
"corpus",
"autoencoder",
"supervised",
"classifiers",
"cnn"
],
[
"softmax",
"learning",
"backpropagation",
"autoencoders",
"modeling",
"rnns",
"learns",
"neural",
"models",
"rnn"
],
[
"imagenet",
"networks",
"regularization",
"neural",
"neuron",
"backpropagation",
"cnns",
"cnn",
"softmax",
"adversarial"
],
[
"classifier",
"classifiers",
"classifying",
"classification",
"classify",
"supervised",
"softmax",
"ensemble",
"boosting",
"ensembles"
],
[
"predict",
"forecasts",
"forecasting",
"forecast",
"softmax",
"predicts",
"rnns",
"prediction",
"predicting",
"lstm"
],
[
"classifier",
"classification",
"lstm",
"classifiers",
"classifying",
"softmax",
"classify",
"supervised",
"recognition",
"neural"
],
[
"classifiers",
"predicting",
"softmax",
"supervised",
"datasets",
"classifying",
"classification",
"classifier",
"dataset",
"classify"
],
[
"networks",
"nodes",
"graphs",
"graph",
"cnn",
"cnns",
"embeddings",
"softmax",
"vertex",
"supervised"
],
[
"gan",
"gans",
"generative",
"adversarial",
"autoencoders",
"adversarially",
"cnn",
"imagenet",
"autoencoder",
"cnns"
],
[
"optimization",
"minimization",
"optimizer",
"optimizing",
"lasso",
"minimize",
"regularization",
"optimal",
"softmax",
"optimize"
],
[
"softmax",
"autoencoders",
"supervised",
"models",
"rnns",
"generative",
"backpropagation",
"probabilistic",
"priors",
"learns"
],
[
"autoencoders",
"imagenet",
"benchmarks",
"bottleneck",
"cnn",
"rnns",
"cnns",
"networks",
"neural",
"optimized"
],
[
"cnn",
"imagenet",
"autoencoders",
"neural",
"supervised",
"softmax",
"cnns",
"recognition",
"segmentation",
"recognizing"
],
[
"cnn",
"imagenet",
"cnns",
"recognition",
"convolutions",
"autoencoders",
"recognizing",
"supervised",
"softmax",
"convolutional"
],
[
"supervised",
"metrics",
"classifiers",
"classifying",
"softmax",
"classification",
"classifier",
"regularization",
"metric",
"similarity"
],
[
"regularized",
"lasso",
"sparse",
"regularization",
"minimization",
"softmax",
"tensors",
"optimizer",
"clustering",
"tensor"
]
] | 588.672674 | all-MiniLM-L6-v2 | 0.455 | -0.250703 | 0.178586 | 0.840558 |
ArXiv ML Papers | Top2Vec | 46 | 20 | [
[
"learning",
"ai",
"planning",
"reinforcement",
"bandit",
"learnt",
"learns",
"bandits",
"learnable",
"softmax"
],
[
"adversarially",
"distributed",
"softmax",
"federated",
"learning",
"learns",
"learnt",
"supervised",
"rnns",
"rnn"
],
[
"adversarial",
"adversarially",
"cnns",
"adversary",
"cnn",
"adversaries",
"attacks",
"softmax",
"classifiers",
"autoencoders"
],
[
"fairness",
"adversarially",
"bias",
"biases",
"classifiers",
"discrimination",
"adversarial",
"discriminate",
"supervised",
"classifier"
],
[
"softmax",
"autoencoder",
"lstm",
"supervised",
"rnns",
"embeddings",
"learns",
"corpus",
"autoencoders",
"cnn"
],
[
"learns",
"rnns",
"neural",
"backpropagation",
"learning",
"softmax",
"autoencoders",
"models",
"rnn",
"modeling"
],
[
"forecasts",
"forecasting",
"forecast",
"prediction",
"predict",
"predicting",
"predicts",
"softmax",
"classifiers",
"rnns"
],
[
"softmax",
"networks",
"neural",
"backpropagation",
"cnns",
"imagenet",
"regularization",
"cnn",
"neuron",
"rnns"
],
[
"classify",
"classifying",
"eeg",
"supervised",
"classifiers",
"classifier",
"classification",
"recognition",
"lstm",
"svm"
],
[
"networks",
"graphs",
"nodes",
"graph",
"cnn",
"softmax",
"cnns",
"supervised",
"vertex",
"embeddings"
],
[
"dataset",
"classifying",
"classifiers",
"predicting",
"datasets",
"softmax",
"supervised",
"classification",
"classify",
"rnns"
],
[
"classifiers",
"ensemble",
"softmax",
"boosting",
"supervised",
"classification",
"classifying",
"classify",
"classifier",
"ensembles"
],
[
"imagenet",
"cnns",
"cnn",
"supervised",
"softmax",
"neural",
"recognition",
"autoencoders",
"segmentation",
"recognizing"
],
[
"regularization",
"classifiers",
"metrics",
"classifier",
"classifying",
"classification",
"softmax",
"supervised",
"similarity",
"metric"
],
[
"sparse",
"softmax",
"clustering",
"minimization",
"optimizer",
"lasso",
"algorithms",
"regularized",
"regularization",
"supervised"
],
[
"gan",
"autoencoders",
"adversarially",
"adversarial",
"autoencoder",
"generative",
"imagenet",
"gans",
"cnns",
"cnn"
],
[
"cnns",
"rnns",
"cnn",
"imagenet",
"autoencoders",
"bottleneck",
"benchmarks",
"networks",
"softmax",
"neural"
],
[
"imagenet",
"recognizing",
"recognition",
"convolutions",
"cnns",
"cnn",
"supervised",
"autoencoders",
"convolutional",
"softmax"
],
[
"optimization",
"minimization",
"optimizer",
"optimizing",
"optimize",
"minimize",
"regularization",
"lasso",
"optimized",
"optimizes"
],
[
"autoencoders",
"learns",
"rnns",
"generative",
"softmax",
"models",
"supervised",
"backpropagation",
"priors",
"autoencoder"
]
] | 631.574545 | all-MiniLM-L6-v2 | 0.45 | -0.259184 | 0.173666 | 0.84807 |
ArXiv ML Papers | Top2Vec | 43 | 30 | [
[
"adversarially",
"classifiers",
"discrimination",
"bias",
"biases",
"fairness",
"supervised",
"adversarial",
"discriminate",
"classifier"
],
[
"softmax",
"distributed",
"learns",
"federated",
"adversarially",
"learnt",
"learning",
"supervised",
"rnns",
"rnn"
],
[
"adversary",
"security",
"private",
"adversarial",
"softmax",
"regularization",
"classifiers",
"randomized",
"adversarially",
"privacy"
],
[
"adversary",
"cnns",
"attacks",
"adversarial",
"adversarially",
"cnn",
"classifiers",
"adversaries",
"softmax",
"attacker"
],
[
"learnt",
"learns",
"ai",
"bandit",
"bandits",
"learnable",
"reinforcement",
"learning",
"planning",
"softmax"
],
[
"backpropagation",
"rnns",
"learns",
"softmax",
"neural",
"modeling",
"models",
"autoencoders",
"rnn",
"learning"
],
[
"interpretability",
"classifiers",
"adversarially",
"explanations",
"softmax",
"adversarial",
"supervised",
"neural",
"cnns",
"cnn"
],
[
"personalized",
"factorization",
"recommender",
"recommendation",
"softmax",
"recommendations",
"embeddings",
"retrieval",
"supervised",
"ranking"
],
[
"lstm",
"corpus",
"learns",
"cnn",
"autoencoders",
"autoencoder",
"rnns",
"softmax",
"neural",
"cnns"
],
[
"neural",
"cnns",
"softmax",
"backpropagation",
"networks",
"regularization",
"rnns",
"imagenet",
"autoencoders",
"cnn"
],
[
"softmax",
"forecasts",
"predicts",
"predict",
"prediction",
"forecasting",
"predicting",
"forecast",
"classifiers",
"rnns"
],
[
"classify",
"classifying",
"neural",
"classification",
"classifier",
"softmax",
"lstm",
"classifiers",
"autoencoders",
"recognition"
],
[
"classifiers",
"classification",
"supervised",
"lasso",
"sparse",
"softmax",
"classifying",
"regularization",
"svm",
"regularized"
],
[
"nodes",
"graphs",
"graph",
"networks",
"cnn",
"cnns",
"vertex",
"embeddings",
"softmax",
"supervised"
],
[
"supervised",
"predicting",
"dataset",
"datasets",
"classifying",
"classify",
"classifiers",
"classifier",
"classification",
"softmax"
],
[
"gan",
"gans",
"adversarial",
"generative",
"autoencoders",
"adversarially",
"cnn",
"imagenet",
"autoencoder",
"cnns"
],
[
"imagenet",
"cnn",
"cnns",
"supervised",
"softmax",
"recognition",
"autoencoders",
"neural",
"segmentation",
"recognizing"
],
[
"bottleneck",
"benchmarks",
"autoencoder",
"networks",
"rnns",
"imagenet",
"autoencoders",
"cnns",
"cnn",
"optimized"
],
[
"convolutions",
"autoencoders",
"recognizing",
"supervised",
"imagenet",
"cnns",
"recognition",
"cnn",
"convolutional",
"softmax"
],
[
"supervised",
"softmax",
"generative",
"autoencoders",
"rnns",
"probabilistic",
"learns",
"backpropagation",
"models",
"priors"
],
[
"optimization",
"optimizer",
"regularization",
"lasso",
"minimize",
"minimization",
"optimizing",
"optimize",
"optimized",
"optimal"
]
] | 577.164902 | all-MiniLM-L6-v2 | 0.452381 | -0.264192 | 0.17378 | 0.84458 |
ArXiv ML Papers | Top2Vec | 44 | 30 | [
[
"distributed",
"softmax",
"federated",
"learnt",
"learns",
"learning",
"supervised",
"adversarially",
"rnns",
"rnn"
],
[
"classifiers",
"adversary",
"cnns",
"cnn",
"attacks",
"adversarial",
"adversarially",
"softmax",
"exploiting",
"adversaries"
],
[
"fairness",
"biases",
"classifiers",
"classifier",
"adversarial",
"discrimination",
"discriminate",
"adversarially",
"supervised",
"bias"
],
[
"supervised",
"leveraging",
"inference",
"estimating",
"interventions",
"causal",
"models",
"priors",
"generalization",
"observational"
],
[
"ai",
"reinforcement",
"bandit",
"planning",
"learning",
"learns",
"learnt",
"bandits",
"learnable",
"softmax"
],
[
"backpropagation",
"rnns",
"softmax",
"neural",
"autoencoders",
"learns",
"models",
"learning",
"modeling",
"rnn"
],
[
"softmax",
"neural",
"classifying",
"classify",
"autoencoders",
"classifiers",
"classifier",
"classification",
"supervised",
"lstm"
],
[
"graphs",
"softmax",
"supervised",
"networks",
"cnn",
"nodes",
"graph",
"cnns",
"embeddings",
"rnns"
],
[
"cnns",
"cnn",
"imagenet",
"softmax",
"regularization",
"neural",
"networks",
"backpropagation",
"autoencoders",
"rnns"
],
[
"predicting",
"forecasts",
"forecasting",
"forecast",
"prediction",
"softmax",
"rnns",
"classifiers",
"predicts",
"predict"
],
[
"embeddings",
"recommender",
"recommendations",
"recommendation",
"supervised",
"softmax",
"factorization",
"personalized",
"ranking",
"retrieval"
],
[
"autoencoder",
"softmax",
"corpus",
"learns",
"rnns",
"cnn",
"neural",
"autoencoders",
"lstm",
"cnns"
],
[
"classifier",
"classifying",
"boosting",
"supervised",
"classify",
"ensemble",
"classification",
"classifiers",
"ensembles",
"softmax"
],
[
"classification",
"metrics",
"classifiers",
"supervised",
"softmax",
"metric",
"similarity",
"classifying",
"classifier",
"regularization"
],
[
"optimizer",
"regularization",
"sparse",
"lasso",
"regularized",
"clustering",
"tensors",
"softmax",
"minimization",
"algorithms"
],
[
"autoencoders",
"imagenet",
"benchmarks",
"bottleneck",
"cnn",
"rnns",
"cnns",
"networks",
"neural",
"optimized"
],
[
"imagenet",
"generative",
"cnn",
"autoencoder",
"autoencoders",
"gans",
"adversarially",
"adversarial",
"gan",
"cnns"
],
[
"lasso",
"optimizer",
"optimize",
"optimization",
"minimization",
"minimize",
"regularization",
"optimizing",
"optimized",
"optimizes"
],
[
"supervised",
"learns",
"priors",
"autoencoders",
"backpropagation",
"probabilistic",
"rnns",
"softmax",
"generative",
"models"
],
[
"imagenet",
"cnn",
"cnns",
"recognition",
"softmax",
"supervised",
"autoencoders",
"neural",
"segmentation",
"recognizing"
],
[
"recognizing",
"supervised",
"convolutions",
"imagenet",
"cnns",
"recognition",
"cnn",
"autoencoders",
"convolutional",
"classifiers"
]
] | 601.105897 | all-MiniLM-L6-v2 | 0.480952 | -0.2564 | 0.172465 | 0.844825 |
ArXiv ML Papers | Top2Vec | 45 | 30 | [
[
"learning",
"learns",
"learnt",
"reinforcement",
"bandit",
"bandits",
"ai",
"planning",
"learnable",
"softmax"
],
[
"softmax",
"distributed",
"learns",
"federated",
"adversarially",
"learnt",
"learning",
"supervised",
"rnns",
"rnn"
],
[
"adversary",
"adversarial",
"adversarially",
"cnns",
"classifiers",
"softmax",
"cnn",
"exploiting",
"attacks",
"adversaries"
],
[
"biases",
"bias",
"fairness",
"adversarially",
"classifiers",
"adversarial",
"supervised",
"discrimination",
"discriminate",
"classifier"
],
[
"rnns",
"lstm",
"learns",
"autoencoders",
"softmax",
"corpus",
"autoencoder",
"supervised",
"classifiers",
"cnn"
],
[
"softmax",
"learning",
"backpropagation",
"autoencoders",
"modeling",
"rnns",
"learns",
"neural",
"models",
"rnn"
],
[
"imagenet",
"networks",
"regularization",
"neural",
"neuron",
"backpropagation",
"cnns",
"cnn",
"softmax",
"adversarial"
],
[
"classifier",
"classifiers",
"classifying",
"classification",
"classify",
"supervised",
"softmax",
"ensemble",
"boosting",
"ensembles"
],
[
"predict",
"forecasts",
"forecasting",
"forecast",
"softmax",
"predicts",
"rnns",
"prediction",
"predicting",
"lstm"
],
[
"classifier",
"classification",
"lstm",
"classifiers",
"classifying",
"softmax",
"classify",
"supervised",
"recognition",
"neural"
],
[
"classifiers",
"predicting",
"softmax",
"supervised",
"datasets",
"classifying",
"classification",
"classifier",
"dataset",
"classify"
],
[
"networks",
"nodes",
"graphs",
"graph",
"cnn",
"cnns",
"embeddings",
"softmax",
"vertex",
"supervised"
],
[
"gan",
"gans",
"generative",
"adversarial",
"autoencoders",
"adversarially",
"cnn",
"imagenet",
"autoencoder",
"cnns"
],
[
"optimization",
"minimization",
"optimizer",
"optimizing",
"lasso",
"minimize",
"regularization",
"optimal",
"softmax",
"optimize"
],
[
"softmax",
"autoencoders",
"supervised",
"models",
"rnns",
"generative",
"backpropagation",
"probabilistic",
"priors",
"learns"
],
[
"autoencoders",
"imagenet",
"benchmarks",
"bottleneck",
"cnn",
"rnns",
"cnns",
"networks",
"neural",
"optimized"
],
[
"cnn",
"imagenet",
"autoencoders",
"neural",
"supervised",
"softmax",
"cnns",
"recognition",
"segmentation",
"recognizing"
],
[
"cnns",
"recognition",
"imagenet",
"cnn",
"convolutions",
"recognizing",
"supervised",
"autoencoders",
"softmax",
"classifiers"
],
[
"maps",
"cnns",
"cnn",
"imagenet",
"3d",
"recognition",
"recognizing",
"convolutions",
"supervised",
"vision"
],
[
"supervised",
"metrics",
"classifiers",
"classifying",
"softmax",
"classification",
"classifier",
"regularization",
"metric",
"similarity"
],
[
"regularized",
"lasso",
"sparse",
"regularization",
"minimization",
"softmax",
"tensors",
"optimizer",
"clustering",
"tensor"
]
] | 597.81598 | all-MiniLM-L6-v2 | 0.442857 | -0.252986 | 0.175005 | 0.843511 |
ArXiv ML Papers | Top2Vec | 46 | 30 | [
[
"learning",
"ai",
"planning",
"reinforcement",
"bandit",
"learnt",
"learns",
"bandits",
"learnable",
"softmax"
],
[
"adversarially",
"distributed",
"softmax",
"federated",
"learning",
"learns",
"learnt",
"supervised",
"rnns",
"rnn"
],
[
"adversary",
"adversarial",
"adversarially",
"cnns",
"adversaries",
"cnn",
"classifiers",
"attacks",
"softmax",
"attacker"
],
[
"regularization",
"adversarially",
"adversarial",
"privacy",
"private",
"randomized",
"adversary",
"normalization",
"regularized",
"softmax"
],
[
"fairness",
"adversarially",
"bias",
"biases",
"classifiers",
"discrimination",
"adversarial",
"discriminate",
"supervised",
"classifier"
],
[
"softmax",
"autoencoder",
"lstm",
"supervised",
"rnns",
"embeddings",
"learns",
"corpus",
"autoencoders",
"cnn"
],
[
"learns",
"rnns",
"neural",
"backpropagation",
"learning",
"softmax",
"autoencoders",
"models",
"rnn",
"modeling"
],
[
"forecasts",
"forecasting",
"forecast",
"prediction",
"predict",
"predicting",
"predicts",
"softmax",
"classifiers",
"rnns"
],
[
"softmax",
"networks",
"neural",
"backpropagation",
"cnns",
"imagenet",
"regularization",
"cnn",
"neuron",
"rnns"
],
[
"classify",
"classifying",
"eeg",
"supervised",
"classifiers",
"classifier",
"classification",
"recognition",
"lstm",
"svm"
],
[
"networks",
"graphs",
"nodes",
"graph",
"cnn",
"softmax",
"cnns",
"supervised",
"vertex",
"embeddings"
],
[
"dataset",
"classifying",
"classifiers",
"predicting",
"datasets",
"softmax",
"supervised",
"classification",
"classify",
"rnns"
],
[
"classifiers",
"ensemble",
"softmax",
"boosting",
"supervised",
"classification",
"classifying",
"classify",
"classifier",
"ensembles"
],
[
"imagenet",
"cnns",
"cnn",
"supervised",
"softmax",
"neural",
"recognition",
"autoencoders",
"segmentation",
"recognizing"
],
[
"regularization",
"classifiers",
"metrics",
"classifier",
"classifying",
"classification",
"softmax",
"supervised",
"similarity",
"metric"
],
[
"sparse",
"softmax",
"clustering",
"minimization",
"optimizer",
"lasso",
"algorithms",
"regularized",
"regularization",
"supervised"
],
[
"gan",
"autoencoders",
"adversarially",
"adversarial",
"autoencoder",
"generative",
"imagenet",
"gans",
"cnns",
"cnn"
],
[
"cnns",
"rnns",
"cnn",
"imagenet",
"autoencoders",
"bottleneck",
"benchmarks",
"networks",
"softmax",
"neural"
],
[
"imagenet",
"recognizing",
"recognition",
"convolutions",
"cnns",
"cnn",
"supervised",
"autoencoders",
"convolutional",
"softmax"
],
[
"optimization",
"minimization",
"optimizer",
"optimizing",
"optimize",
"minimize",
"regularization",
"lasso",
"optimized",
"optimizes"
],
[
"autoencoders",
"learns",
"rnns",
"generative",
"softmax",
"models",
"supervised",
"backpropagation",
"priors",
"autoencoder"
]
] | 498.065006 | all-MiniLM-L6-v2 | 0.452381 | -0.262968 | 0.171184 | 0.850088 |
ArXiv ML Papers | Top2Vec | 43 | 40 | [
[
"adversarially",
"classifiers",
"discrimination",
"bias",
"biases",
"fairness",
"supervised",
"adversarial",
"discriminate",
"classifier"
],
[
"softmax",
"distributed",
"learns",
"federated",
"adversarially",
"learnt",
"learning",
"supervised",
"rnns",
"rnn"
],
[
"adversary",
"security",
"private",
"adversarial",
"softmax",
"regularization",
"classifiers",
"randomized",
"adversarially",
"privacy"
],
[
"adversary",
"cnns",
"attacks",
"adversarial",
"adversarially",
"cnn",
"classifiers",
"adversaries",
"softmax",
"attacker"
],
[
"learnt",
"learns",
"ai",
"bandit",
"bandits",
"learnable",
"reinforcement",
"learning",
"planning",
"softmax"
],
[
"backpropagation",
"rnns",
"learns",
"softmax",
"neural",
"modeling",
"models",
"autoencoders",
"rnn",
"learning"
],
[
"interpretability",
"classifiers",
"adversarially",
"explanations",
"softmax",
"adversarial",
"supervised",
"neural",
"cnns",
"cnn"
],
[
"personalized",
"factorization",
"recommender",
"recommendation",
"softmax",
"recommendations",
"embeddings",
"retrieval",
"supervised",
"ranking"
],
[
"lstm",
"corpus",
"learns",
"cnn",
"autoencoders",
"autoencoder",
"rnns",
"softmax",
"neural",
"cnns"
],
[
"neural",
"cnns",
"softmax",
"backpropagation",
"networks",
"regularization",
"rnns",
"imagenet",
"autoencoders",
"cnn"
],
[
"softmax",
"forecasts",
"predicts",
"predict",
"prediction",
"forecasting",
"predicting",
"forecast",
"classifiers",
"rnns"
],
[
"classify",
"classifying",
"neural",
"classification",
"classifier",
"softmax",
"lstm",
"classifiers",
"autoencoders",
"recognition"
],
[
"classifiers",
"classification",
"supervised",
"lasso",
"sparse",
"softmax",
"classifying",
"regularization",
"svm",
"regularized"
],
[
"nodes",
"graphs",
"graph",
"networks",
"cnn",
"cnns",
"vertex",
"embeddings",
"softmax",
"supervised"
],
[
"supervised",
"predicting",
"dataset",
"datasets",
"classifying",
"classify",
"classifiers",
"classifier",
"classification",
"softmax"
],
[
"gan",
"gans",
"adversarial",
"generative",
"autoencoders",
"adversarially",
"cnn",
"imagenet",
"autoencoder",
"cnns"
],
[
"imagenet",
"cnn",
"cnns",
"supervised",
"softmax",
"recognition",
"autoencoders",
"neural",
"segmentation",
"recognizing"
],
[
"bottleneck",
"benchmarks",
"autoencoder",
"networks",
"rnns",
"imagenet",
"autoencoders",
"cnns",
"cnn",
"optimized"
],
[
"convolutions",
"autoencoders",
"recognizing",
"supervised",
"imagenet",
"cnns",
"recognition",
"cnn",
"convolutional",
"softmax"
],
[
"supervised",
"softmax",
"generative",
"autoencoders",
"rnns",
"probabilistic",
"learns",
"backpropagation",
"models",
"priors"
],
[
"optimization",
"optimizer",
"regularization",
"lasso",
"minimize",
"minimization",
"optimizing",
"optimize",
"optimized",
"optimal"
]
] | 640.464483 | all-MiniLM-L6-v2 | 0.452381 | -0.264192 | 0.17378 | 0.845774 |
ArXiv ML Papers | Top2Vec | 44 | 40 | [
[
"distributed",
"softmax",
"federated",
"learnt",
"learns",
"learning",
"supervised",
"adversarially",
"rnns",
"rnn"
],
[
"classifiers",
"adversary",
"cnns",
"cnn",
"attacks",
"adversarial",
"adversarially",
"softmax",
"exploiting",
"adversaries"
],
[
"fairness",
"biases",
"classifiers",
"classifier",
"adversarial",
"discrimination",
"discriminate",
"adversarially",
"supervised",
"bias"
],
[
"supervised",
"leveraging",
"inference",
"estimating",
"interventions",
"causal",
"models",
"priors",
"generalization",
"observational"
],
[
"ai",
"reinforcement",
"bandit",
"planning",
"learning",
"learns",
"learnt",
"bandits",
"learnable",
"softmax"
],
[
"backpropagation",
"rnns",
"softmax",
"neural",
"autoencoders",
"learns",
"models",
"learning",
"modeling",
"rnn"
],
[
"softmax",
"neural",
"classifying",
"classify",
"autoencoders",
"classifiers",
"classifier",
"classification",
"supervised",
"lstm"
],
[
"graphs",
"softmax",
"supervised",
"networks",
"cnn",
"nodes",
"graph",
"cnns",
"embeddings",
"rnns"
],
[
"cnns",
"cnn",
"imagenet",
"softmax",
"regularization",
"neural",
"networks",
"backpropagation",
"autoencoders",
"rnns"
],
[
"predicting",
"forecasts",
"forecasting",
"forecast",
"prediction",
"softmax",
"rnns",
"classifiers",
"predicts",
"predict"
],
[
"embeddings",
"recommender",
"recommendations",
"recommendation",
"supervised",
"softmax",
"factorization",
"personalized",
"ranking",
"retrieval"
],
[
"autoencoder",
"softmax",
"corpus",
"learns",
"rnns",
"cnn",
"neural",
"autoencoders",
"lstm",
"cnns"
],
[
"classifier",
"classifying",
"boosting",
"supervised",
"classify",
"ensemble",
"classification",
"classifiers",
"ensembles",
"softmax"
],
[
"classification",
"metrics",
"classifiers",
"supervised",
"softmax",
"metric",
"similarity",
"classifying",
"classifier",
"regularization"
],
[
"optimizer",
"regularization",
"sparse",
"lasso",
"regularized",
"clustering",
"tensors",
"softmax",
"minimization",
"algorithms"
],
[
"autoencoders",
"imagenet",
"benchmarks",
"bottleneck",
"cnn",
"rnns",
"cnns",
"networks",
"neural",
"optimized"
],
[
"imagenet",
"generative",
"cnn",
"autoencoder",
"autoencoders",
"gans",
"adversarially",
"adversarial",
"gan",
"cnns"
],
[
"lasso",
"optimizer",
"optimize",
"optimization",
"minimization",
"minimize",
"regularization",
"optimizing",
"optimized",
"optimizes"
],
[
"supervised",
"learns",
"priors",
"autoencoders",
"backpropagation",
"probabilistic",
"rnns",
"softmax",
"generative",
"models"
],
[
"imagenet",
"cnn",
"cnns",
"recognition",
"softmax",
"supervised",
"autoencoders",
"neural",
"segmentation",
"recognizing"
],
[
"recognizing",
"supervised",
"convolutions",
"imagenet",
"cnns",
"recognition",
"cnn",
"autoencoders",
"convolutional",
"classifiers"
]
] | 569.045129 | all-MiniLM-L6-v2 | 0.480952 | -0.2564 | 0.172465 | 0.846093 |
ArXiv ML Papers | Top2Vec | 45 | 40 | [
[
"learning",
"learns",
"learnt",
"reinforcement",
"bandit",
"bandits",
"ai",
"planning",
"learnable",
"softmax"
],
[
"softmax",
"distributed",
"learns",
"federated",
"adversarially",
"learnt",
"learning",
"supervised",
"rnns",
"rnn"
],
[
"adversary",
"adversarial",
"adversarially",
"cnns",
"classifiers",
"softmax",
"cnn",
"exploiting",
"attacks",
"adversaries"
],
[
"biases",
"bias",
"fairness",
"adversarially",
"classifiers",
"adversarial",
"supervised",
"discrimination",
"discriminate",
"classifier"
],
[
"rnns",
"lstm",
"learns",
"autoencoders",
"softmax",
"corpus",
"autoencoder",
"supervised",
"classifiers",
"cnn"
],
[
"softmax",
"learning",
"backpropagation",
"autoencoders",
"modeling",
"rnns",
"learns",
"neural",
"models",
"rnn"
],
[
"imagenet",
"networks",
"regularization",
"neural",
"neuron",
"backpropagation",
"cnns",
"cnn",
"softmax",
"adversarial"
],
[
"classifier",
"classifiers",
"classifying",
"classification",
"classify",
"supervised",
"softmax",
"ensemble",
"boosting",
"ensembles"
],
[
"predict",
"forecasts",
"forecasting",
"forecast",
"softmax",
"predicts",
"rnns",
"prediction",
"predicting",
"lstm"
],
[
"classifier",
"classification",
"lstm",
"classifiers",
"classifying",
"softmax",
"classify",
"supervised",
"recognition",
"neural"
],
[
"classifiers",
"predicting",
"softmax",
"supervised",
"datasets",
"classifying",
"classification",
"classifier",
"dataset",
"classify"
],
[
"networks",
"nodes",
"graphs",
"graph",
"cnn",
"cnns",
"embeddings",
"softmax",
"vertex",
"supervised"
],
[
"gan",
"gans",
"generative",
"adversarial",
"autoencoders",
"adversarially",
"cnn",
"imagenet",
"autoencoder",
"cnns"
],
[
"optimization",
"minimization",
"optimizer",
"optimizing",
"lasso",
"minimize",
"regularization",
"optimal",
"softmax",
"optimize"
],
[
"softmax",
"autoencoders",
"supervised",
"models",
"rnns",
"generative",
"backpropagation",
"probabilistic",
"priors",
"learns"
],
[
"autoencoders",
"imagenet",
"benchmarks",
"bottleneck",
"cnn",
"rnns",
"cnns",
"networks",
"neural",
"optimized"
],
[
"cnn",
"imagenet",
"autoencoders",
"neural",
"supervised",
"softmax",
"cnns",
"recognition",
"segmentation",
"recognizing"
],
[
"cnns",
"recognition",
"imagenet",
"cnn",
"convolutions",
"recognizing",
"supervised",
"autoencoders",
"softmax",
"classifiers"
],
[
"maps",
"cnns",
"cnn",
"imagenet",
"3d",
"recognition",
"recognizing",
"convolutions",
"supervised",
"vision"
],
[
"supervised",
"metrics",
"classifiers",
"classifying",
"softmax",
"classification",
"classifier",
"regularization",
"metric",
"similarity"
],
[
"regularized",
"lasso",
"sparse",
"regularization",
"minimization",
"softmax",
"tensors",
"optimizer",
"clustering",
"tensor"
]
] | 569.015667 | all-MiniLM-L6-v2 | 0.442857 | -0.252986 | 0.175005 | 0.842255 |
ArXiv ML Papers | Top2Vec | 46 | 40 | [
[
"learning",
"ai",
"planning",
"reinforcement",
"bandit",
"learnt",
"learns",
"bandits",
"learnable",
"softmax"
],
[
"adversarially",
"distributed",
"softmax",
"federated",
"learning",
"learns",
"learnt",
"supervised",
"rnns",
"rnn"
],
[
"adversary",
"adversarial",
"adversarially",
"cnns",
"adversaries",
"cnn",
"classifiers",
"attacks",
"softmax",
"attacker"
],
[
"regularization",
"adversarially",
"adversarial",
"privacy",
"private",
"randomized",
"adversary",
"normalization",
"regularized",
"softmax"
],
[
"fairness",
"adversarially",
"bias",
"biases",
"classifiers",
"discrimination",
"adversarial",
"discriminate",
"supervised",
"classifier"
],
[
"softmax",
"autoencoder",
"lstm",
"supervised",
"rnns",
"embeddings",
"learns",
"corpus",
"autoencoders",
"cnn"
],
[
"learns",
"rnns",
"neural",
"backpropagation",
"learning",
"softmax",
"autoencoders",
"models",
"rnn",
"modeling"
],
[
"forecasts",
"forecasting",
"forecast",
"prediction",
"predict",
"predicting",
"predicts",
"softmax",
"classifiers",
"rnns"
],
[
"softmax",
"networks",
"neural",
"backpropagation",
"cnns",
"imagenet",
"regularization",
"cnn",
"neuron",
"rnns"
],
[
"classify",
"classifying",
"eeg",
"supervised",
"classifiers",
"classifier",
"classification",
"recognition",
"lstm",
"svm"
],
[
"networks",
"graphs",
"nodes",
"graph",
"cnn",
"softmax",
"cnns",
"supervised",
"vertex",
"embeddings"
],
[
"dataset",
"classifying",
"classifiers",
"predicting",
"datasets",
"softmax",
"supervised",
"classification",
"classify",
"rnns"
],
[
"classifiers",
"ensemble",
"softmax",
"boosting",
"supervised",
"classification",
"classifying",
"classify",
"classifier",
"ensembles"
],
[
"imagenet",
"cnns",
"cnn",
"supervised",
"softmax",
"neural",
"recognition",
"autoencoders",
"segmentation",
"recognizing"
],
[
"regularization",
"classifiers",
"metrics",
"classifier",
"classifying",
"classification",
"softmax",
"supervised",
"similarity",
"metric"
],
[
"sparse",
"softmax",
"clustering",
"minimization",
"optimizer",
"lasso",
"algorithms",
"regularized",
"regularization",
"supervised"
],
[
"gan",
"autoencoders",
"adversarially",
"adversarial",
"autoencoder",
"generative",
"imagenet",
"gans",
"cnns",
"cnn"
],
[
"cnns",
"rnns",
"cnn",
"imagenet",
"autoencoders",
"bottleneck",
"benchmarks",
"networks",
"softmax",
"neural"
],
[
"imagenet",
"recognizing",
"recognition",
"convolutions",
"cnns",
"cnn",
"supervised",
"autoencoders",
"convolutional",
"softmax"
],
[
"optimization",
"minimization",
"optimizer",
"optimizing",
"optimize",
"minimize",
"regularization",
"lasso",
"optimized",
"optimizes"
],
[
"autoencoders",
"learns",
"rnns",
"generative",
"softmax",
"models",
"supervised",
"backpropagation",
"priors",
"autoencoder"
]
] | 584.353214 | all-MiniLM-L6-v2 | 0.452381 | -0.262968 | 0.171184 | 0.844559 |
ArXiv ML Papers | Top2Vec | 43 | 50 | [
[
"adversarially",
"classifiers",
"discrimination",
"bias",
"biases",
"fairness",
"supervised",
"adversarial",
"discriminate",
"classifier"
],
[
"softmax",
"distributed",
"learns",
"federated",
"adversarially",
"learnt",
"learning",
"supervised",
"rnns",
"rnn"
],
[
"adversary",
"security",
"private",
"adversarial",
"softmax",
"regularization",
"classifiers",
"randomized",
"adversarially",
"privacy"
],
[
"adversary",
"cnns",
"attacks",
"adversarial",
"adversarially",
"cnn",
"classifiers",
"adversaries",
"softmax",
"attacker"
],
[
"learnt",
"learns",
"ai",
"bandit",
"bandits",
"learnable",
"reinforcement",
"learning",
"planning",
"softmax"
],
[
"backpropagation",
"rnns",
"learns",
"softmax",
"neural",
"modeling",
"models",
"autoencoders",
"rnn",
"learning"
],
[
"interpretability",
"classifiers",
"adversarially",
"explanations",
"softmax",
"adversarial",
"supervised",
"neural",
"cnns",
"cnn"
],
[
"personalized",
"factorization",
"recommender",
"recommendation",
"softmax",
"recommendations",
"embeddings",
"retrieval",
"supervised",
"ranking"
],
[
"lstm",
"corpus",
"learns",
"cnn",
"autoencoders",
"autoencoder",
"rnns",
"softmax",
"neural",
"cnns"
],
[
"neural",
"cnns",
"softmax",
"backpropagation",
"networks",
"regularization",
"rnns",
"imagenet",
"autoencoders",
"cnn"
],
[
"softmax",
"forecasts",
"predicts",
"predict",
"prediction",
"forecasting",
"predicting",
"forecast",
"classifiers",
"rnns"
],
[
"classify",
"classifying",
"neural",
"classification",
"classifier",
"softmax",
"lstm",
"classifiers",
"autoencoders",
"recognition"
],
[
"classifiers",
"classification",
"supervised",
"lasso",
"sparse",
"softmax",
"classifying",
"regularization",
"svm",
"regularized"
],
[
"nodes",
"graphs",
"graph",
"networks",
"cnn",
"cnns",
"vertex",
"embeddings",
"softmax",
"supervised"
],
[
"supervised",
"predicting",
"dataset",
"datasets",
"classifying",
"classify",
"classifiers",
"classifier",
"classification",
"softmax"
],
[
"gan",
"gans",
"adversarial",
"generative",
"autoencoders",
"adversarially",
"cnn",
"imagenet",
"autoencoder",
"cnns"
],
[
"imagenet",
"cnn",
"cnns",
"supervised",
"softmax",
"recognition",
"autoencoders",
"neural",
"segmentation",
"recognizing"
],
[
"bottleneck",
"benchmarks",
"autoencoder",
"networks",
"rnns",
"imagenet",
"autoencoders",
"cnns",
"cnn",
"optimized"
],
[
"convolutions",
"autoencoders",
"recognizing",
"supervised",
"imagenet",
"cnns",
"recognition",
"cnn",
"convolutional",
"softmax"
],
[
"supervised",
"softmax",
"generative",
"autoencoders",
"rnns",
"probabilistic",
"learns",
"backpropagation",
"models",
"priors"
],
[
"optimization",
"optimizer",
"regularization",
"lasso",
"minimize",
"minimization",
"optimizing",
"optimize",
"optimized",
"optimal"
]
] | 498.800022 | all-MiniLM-L6-v2 | 0.452381 | -0.264192 | 0.17378 | 0.850448 |
ArXiv ML Papers | Top2Vec | 44 | 50 | [
[
"distributed",
"softmax",
"federated",
"learnt",
"learns",
"learning",
"supervised",
"adversarially",
"rnns",
"rnn"
],
[
"classifiers",
"adversary",
"cnns",
"cnn",
"attacks",
"adversarial",
"adversarially",
"softmax",
"exploiting",
"adversaries"
],
[
"fairness",
"biases",
"classifiers",
"classifier",
"adversarial",
"discrimination",
"discriminate",
"adversarially",
"supervised",
"bias"
],
[
"supervised",
"leveraging",
"inference",
"estimating",
"interventions",
"causal",
"models",
"priors",
"generalization",
"observational"
],
[
"ai",
"reinforcement",
"bandit",
"planning",
"learning",
"learns",
"learnt",
"bandits",
"learnable",
"softmax"
],
[
"backpropagation",
"rnns",
"softmax",
"neural",
"autoencoders",
"learns",
"models",
"learning",
"modeling",
"rnn"
],
[
"softmax",
"neural",
"classifying",
"classify",
"autoencoders",
"classifiers",
"classifier",
"classification",
"supervised",
"lstm"
],
[
"graphs",
"softmax",
"supervised",
"networks",
"cnn",
"nodes",
"graph",
"cnns",
"embeddings",
"rnns"
],
[
"cnns",
"cnn",
"imagenet",
"softmax",
"regularization",
"neural",
"networks",
"backpropagation",
"autoencoders",
"rnns"
],
[
"predicting",
"forecasts",
"forecasting",
"forecast",
"prediction",
"softmax",
"rnns",
"classifiers",
"predicts",
"predict"
],
[
"embeddings",
"recommender",
"recommendations",
"recommendation",
"supervised",
"softmax",
"factorization",
"personalized",
"ranking",
"retrieval"
],
[
"autoencoder",
"softmax",
"corpus",
"learns",
"rnns",
"cnn",
"neural",
"autoencoders",
"lstm",
"cnns"
],
[
"classifier",
"classifying",
"boosting",
"supervised",
"classify",
"ensemble",
"classification",
"classifiers",
"ensembles",
"softmax"
],
[
"classification",
"metrics",
"classifiers",
"supervised",
"softmax",
"metric",
"similarity",
"classifying",
"classifier",
"regularization"
],
[
"optimizer",
"regularization",
"sparse",
"lasso",
"regularized",
"clustering",
"tensors",
"softmax",
"minimization",
"algorithms"
],
[
"autoencoders",
"imagenet",
"benchmarks",
"bottleneck",
"cnn",
"rnns",
"cnns",
"networks",
"neural",
"optimized"
],
[
"imagenet",
"generative",
"cnn",
"autoencoder",
"autoencoders",
"gans",
"adversarially",
"adversarial",
"gan",
"cnns"
],
[
"lasso",
"optimizer",
"optimize",
"optimization",
"minimization",
"minimize",
"regularization",
"optimizing",
"optimized",
"optimizes"
],
[
"supervised",
"learns",
"priors",
"autoencoders",
"backpropagation",
"probabilistic",
"rnns",
"softmax",
"generative",
"models"
],
[
"imagenet",
"cnn",
"cnns",
"recognition",
"softmax",
"supervised",
"autoencoders",
"neural",
"segmentation",
"recognizing"
],
[
"recognizing",
"supervised",
"convolutions",
"imagenet",
"cnns",
"recognition",
"cnn",
"autoencoders",
"convolutional",
"classifiers"
]
] | 530.921954 | all-MiniLM-L6-v2 | 0.480952 | -0.2564 | 0.172465 | 0.845788 |
ArXiv ML Papers | Top2Vec | 45 | 50 | [
[
"learning",
"learns",
"learnt",
"reinforcement",
"bandit",
"bandits",
"ai",
"planning",
"learnable",
"softmax"
],
[
"softmax",
"distributed",
"learns",
"federated",
"adversarially",
"learnt",
"learning",
"supervised",
"rnns",
"rnn"
],
[
"adversary",
"adversarial",
"adversarially",
"cnns",
"classifiers",
"softmax",
"cnn",
"exploiting",
"attacks",
"adversaries"
],
[
"biases",
"bias",
"fairness",
"adversarially",
"classifiers",
"adversarial",
"supervised",
"discrimination",
"discriminate",
"classifier"
],
[
"rnns",
"lstm",
"learns",
"autoencoders",
"softmax",
"corpus",
"autoencoder",
"supervised",
"classifiers",
"cnn"
],
[
"softmax",
"learning",
"backpropagation",
"autoencoders",
"modeling",
"rnns",
"learns",
"neural",
"models",
"rnn"
],
[
"imagenet",
"networks",
"regularization",
"neural",
"neuron",
"backpropagation",
"cnns",
"cnn",
"softmax",
"adversarial"
],
[
"classifier",
"classifiers",
"classifying",
"classification",
"classify",
"supervised",
"softmax",
"ensemble",
"boosting",
"ensembles"
],
[
"predict",
"forecasts",
"forecasting",
"forecast",
"softmax",
"predicts",
"rnns",
"prediction",
"predicting",
"lstm"
],
[
"classifier",
"classification",
"lstm",
"classifiers",
"classifying",
"softmax",
"classify",
"supervised",
"recognition",
"neural"
],
[
"classifiers",
"predicting",
"softmax",
"supervised",
"datasets",
"classifying",
"classification",
"classifier",
"dataset",
"classify"
],
[
"networks",
"nodes",
"graphs",
"graph",
"cnn",
"cnns",
"embeddings",
"softmax",
"vertex",
"supervised"
],
[
"gan",
"gans",
"generative",
"adversarial",
"autoencoders",
"adversarially",
"cnn",
"imagenet",
"autoencoder",
"cnns"
],
[
"optimization",
"minimization",
"optimizer",
"optimizing",
"lasso",
"minimize",
"regularization",
"optimal",
"softmax",
"optimize"
],
[
"softmax",
"autoencoders",
"supervised",
"models",
"rnns",
"generative",
"backpropagation",
"probabilistic",
"priors",
"learns"
],
[
"autoencoders",
"imagenet",
"benchmarks",
"bottleneck",
"cnn",
"rnns",
"cnns",
"networks",
"neural",
"optimized"
],
[
"cnn",
"imagenet",
"autoencoders",
"neural",
"supervised",
"softmax",
"cnns",
"recognition",
"segmentation",
"recognizing"
],
[
"cnns",
"recognition",
"imagenet",
"cnn",
"convolutions",
"recognizing",
"supervised",
"autoencoders",
"softmax",
"classifiers"
],
[
"maps",
"cnns",
"cnn",
"imagenet",
"3d",
"recognition",
"recognizing",
"convolutions",
"supervised",
"vision"
],
[
"supervised",
"metrics",
"classifiers",
"classifying",
"softmax",
"classification",
"classifier",
"regularization",
"metric",
"similarity"
],
[
"regularized",
"lasso",
"sparse",
"regularization",
"minimization",
"softmax",
"tensors",
"optimizer",
"clustering",
"tensor"
]
] | 651.461712 | all-MiniLM-L6-v2 | 0.442857 | -0.252986 | 0.175005 | 0.843242 |
ArXiv ML Papers | Top2Vec | 46 | 50 | [
[
"learning",
"ai",
"planning",
"reinforcement",
"bandit",
"learnt",
"learns",
"bandits",
"learnable",
"softmax"
],
[
"adversarially",
"distributed",
"softmax",
"federated",
"learning",
"learns",
"learnt",
"supervised",
"rnns",
"rnn"
],
[
"adversary",
"adversarial",
"adversarially",
"cnns",
"adversaries",
"cnn",
"classifiers",
"attacks",
"softmax",
"attacker"
],
[
"regularization",
"adversarially",
"adversarial",
"privacy",
"private",
"randomized",
"adversary",
"normalization",
"regularized",
"softmax"
],
[
"fairness",
"adversarially",
"bias",
"biases",
"classifiers",
"discrimination",
"adversarial",
"discriminate",
"supervised",
"classifier"
],
[
"softmax",
"autoencoder",
"lstm",
"supervised",
"rnns",
"embeddings",
"learns",
"corpus",
"autoencoders",
"cnn"
],
[
"learns",
"rnns",
"neural",
"backpropagation",
"learning",
"softmax",
"autoencoders",
"models",
"rnn",
"modeling"
],
[
"forecasts",
"forecasting",
"forecast",
"prediction",
"predict",
"predicting",
"predicts",
"softmax",
"classifiers",
"rnns"
],
[
"softmax",
"networks",
"neural",
"backpropagation",
"cnns",
"imagenet",
"regularization",
"cnn",
"neuron",
"rnns"
],
[
"classify",
"classifying",
"eeg",
"supervised",
"classifiers",
"classifier",
"classification",
"recognition",
"lstm",
"svm"
],
[
"networks",
"graphs",
"nodes",
"graph",
"cnn",
"softmax",
"cnns",
"supervised",
"vertex",
"embeddings"
],
[
"dataset",
"classifying",
"classifiers",
"predicting",
"datasets",
"softmax",
"supervised",
"classification",
"classify",
"rnns"
],
[
"classifiers",
"ensemble",
"softmax",
"boosting",
"supervised",
"classification",
"classifying",
"classify",
"classifier",
"ensembles"
],
[
"imagenet",
"cnns",
"cnn",
"supervised",
"softmax",
"neural",
"recognition",
"autoencoders",
"segmentation",
"recognizing"
],
[
"regularization",
"classifiers",
"metrics",
"classifier",
"classifying",
"classification",
"softmax",
"supervised",
"similarity",
"metric"
],
[
"sparse",
"softmax",
"clustering",
"minimization",
"optimizer",
"lasso",
"algorithms",
"regularized",
"regularization",
"supervised"
],
[
"gan",
"autoencoders",
"adversarially",
"adversarial",
"autoencoder",
"generative",
"imagenet",
"gans",
"cnns",
"cnn"
],
[
"cnns",
"rnns",
"cnn",
"imagenet",
"autoencoders",
"bottleneck",
"benchmarks",
"networks",
"softmax",
"neural"
],
[
"imagenet",
"recognizing",
"recognition",
"convolutions",
"cnns",
"cnn",
"supervised",
"autoencoders",
"convolutional",
"softmax"
],
[
"optimization",
"minimization",
"optimizer",
"optimizing",
"optimize",
"minimize",
"regularization",
"lasso",
"optimized",
"optimizes"
],
[
"autoencoders",
"learns",
"rnns",
"generative",
"softmax",
"models",
"supervised",
"backpropagation",
"priors",
"autoencoder"
]
] | 640.067949 | all-MiniLM-L6-v2 | 0.452381 | -0.262968 | 0.171184 | 0.850129 |
ArXiv ML Papers | GMM | 43 | 10 | [
[
"kernel",
"clustering",
"matrix",
"rank",
"series",
"time",
"regression",
"proposed",
"tensor",
"algorithm"
],
[
"gnns",
"link",
"node",
"embedding",
"graphs",
"graph",
"nodes",
"structure",
"networks",
"social"
],
[
"reward",
"rl",
"reinforcement",
"policy",
"agent",
"agents",
"policies",
"games",
"action",
"control"
],
[
"speech",
"text",
"language",
"languages",
"attention",
"word",
"words",
"sequence",
"task",
"natural"
],
[
"images",
"gan",
"gans",
"image",
"generative",
"latent",
"generation",
"generator",
"variational",
"vae"
],
[
"convergence",
"bounds",
"gradient",
"stochastic",
"convex",
"optimization",
"algorithm",
"bound",
"complexity",
"sample"
],
[
"fairness",
"ml",
"label",
"classifier",
"machine",
"classification",
"decision",
"prediction",
"their",
"research"
],
[
"networks",
"deep",
"memory",
"architectures",
"layer",
"systems",
"quantum",
"neural",
"network",
"time"
],
[
"segmentation",
"images",
"object",
"cnn",
"convolutional",
"image",
"deep",
"detection",
"video",
"visual"
],
[
"perturbations",
"against",
"robustness",
"robust",
"attack",
"attacks",
"defense",
"adversarial",
"privacy",
"examples"
]
] | 2.576472 | all-MiniLM-L6-v2 | 0.94 | 0.053246 | 0.1459 | 0.813589 |
ArXiv ML Papers | GMM | 44 | 10 | [
[
"speech",
"recognition",
"speaker",
"acoustic",
"asr",
"audio",
"music",
"signal",
"end",
"quality"
],
[
"posterior",
"variational",
"inference",
"estimation",
"bayesian",
"distribution",
"latent",
"gaussian",
"series",
"processes"
],
[
"classifier",
"classification",
"machine",
"label",
"prediction",
"metric",
"accuracy",
"datasets",
"ml",
"features"
],
[
"reinforcement",
"reward",
"agent",
"agents",
"rl",
"action",
"control",
"policy",
"games",
"policies"
],
[
"attack",
"defense",
"perturbations",
"privacy",
"attacks",
"adversarial",
"robustness",
"against",
"robust",
"examples"
],
[
"graphs",
"node",
"graph",
"nodes",
"embedding",
"link",
"structure",
"gnns",
"embeddings",
"representation"
],
[
"algorithm",
"convex",
"convergence",
"regret",
"bound",
"stochastic",
"bounds",
"algorithms",
"optimization",
"gradient"
],
[
"memory",
"layer",
"network",
"networks",
"neural",
"accuracy",
"architectures",
"deep",
"architecture",
"input"
],
[
"image",
"segmentation",
"object",
"visual",
"images",
"gan",
"convolutional",
"dataset",
"deep",
"detection"
],
[
"sequence",
"sentences",
"word",
"natural",
"language",
"text",
"task",
"words",
"attention",
"semantic"
]
] | 2.836519 | all-MiniLM-L6-v2 | 0.98 | 0.06128 | 0.149774 | 0.825734 |
ArXiv ML Papers | GMM | 45 | 10 | [
[
"detection",
"segmentation",
"visual",
"gan",
"image",
"images",
"object",
"convolutional",
"supervised",
"medical"
],
[
"examples",
"against",
"adversarial",
"defense",
"perturbations",
"attack",
"attacks",
"privacy",
"robust",
"robustness"
],
[
"agent",
"reinforcement",
"reward",
"rl",
"policy",
"agents",
"regret",
"action",
"policies",
"games"
],
[
"text",
"speech",
"attention",
"audio",
"language",
"languages",
"speaker",
"word",
"translation",
"recurrent"
],
[
"quantum",
"posterior",
"parameters",
"variational",
"bayesian",
"physics",
"uncertainty",
"dynamics",
"inference",
"equations"
],
[
"series",
"classifier",
"prediction",
"classification",
"machine",
"time",
"accuracy",
"selection",
"label",
"regression"
],
[
"convergence",
"algorithm",
"convex",
"matrix",
"optimization",
"rank",
"algorithms",
"linear",
"problems",
"gradient"
],
[
"neural",
"layer",
"network",
"networks",
"deep",
"architectures",
"accuracy",
"memory",
"hardware",
"layers"
],
[
"user",
"items",
"language",
"recommendation",
"item",
"knowledge",
"label",
"recommender",
"topic",
"metric"
],
[
"nodes",
"graph",
"graphs",
"node",
"link",
"gnns",
"embedding",
"structure",
"embeddings",
"representation"
]
] | 2.766196 | all-MiniLM-L6-v2 | 0.97 | 0.024243 | 0.165378 | 0.838997 |
ArXiv ML Papers | GMM | 46 | 10 | [
[
"convex",
"matrix",
"algorithm",
"convergence",
"algorithms",
"linear",
"kernel",
"problems",
"rank",
"optimization"
],
[
"nodes",
"graph",
"link",
"node",
"embedding",
"gnns",
"graphs",
"networks",
"structure",
"embeddings"
],
[
"regret",
"reinforcement",
"agent",
"agents",
"control",
"reward",
"policies",
"rl",
"policy",
"games"
],
[
"variational",
"uncertainty",
"distribution",
"parameters",
"dynamics",
"bayesian",
"neural",
"posterior",
"latent",
"estimation"
],
[
"language",
"natural",
"word",
"text",
"sentences",
"sequence",
"words",
"task",
"representations",
"semantic"
],
[
"object",
"images",
"segmentation",
"image",
"visual",
"medical",
"detection",
"gan",
"supervised",
"convolutional"
],
[
"attack",
"robustness",
"attacks",
"adversarial",
"privacy",
"against",
"perturbations",
"defense",
"examples",
"robust"
],
[
"audio",
"speaker",
"recognition",
"acoustic",
"speech",
"asr",
"music",
"signals",
"signal",
"end"
],
[
"series",
"machine",
"time",
"classification",
"classifier",
"prediction",
"label",
"fairness",
"ml",
"regression"
],
[
"neural",
"memory",
"network",
"hardware",
"architectures",
"accuracy",
"networks",
"deep",
"layer",
"energy"
]
] | 2.882266 | all-MiniLM-L6-v2 | 0.98 | 0.054135 | 0.155939 | 0.826401 |
ArXiv ML Papers | GMM | 43 | 20 | [
[
"algorithm",
"rank",
"tensor",
"matrix",
"low",
"kernel",
"clustering",
"sparse",
"linear",
"subspace"
],
[
"graph",
"link",
"embedding",
"node",
"gnns",
"nodes",
"graphs",
"structure",
"embeddings",
"representation"
],
[
"bound",
"online",
"regret",
"bandits",
"bandit",
"arm",
"sqrt",
"reward",
"agents",
"algorithm"
],
[
"user",
"recommendation",
"language",
"text",
"word",
"item",
"recommender",
"users",
"items",
"words"
],
[
"image",
"generator",
"gans",
"images",
"generative",
"gan",
"latent",
"vae",
"variational",
"generation"
],
[
"convex",
"stochastic",
"gradient",
"optimization",
"convergence",
"descent",
"sgd",
"optimal",
"function",
"problems"
],
[
"communication",
"devices",
"iot",
"distributed",
"traffic",
"vehicle",
"wireless",
"federated",
"computing",
"server"
],
[
"equations",
"physics",
"quantum",
"neural",
"forecasting",
"dynamics",
"series",
"systems",
"time",
"parameters"
],
[
"segmentation",
"medical",
"cancer",
"image",
"imaging",
"ct",
"images",
"patients",
"covid",
"net"
],
[
"fair",
"federated",
"private",
"privacy",
"user",
"fairness",
"sensitive",
"differential",
"local",
"differentially"
],
[
"series",
"ml",
"day",
"software",
"time",
"machine",
"classification",
"study",
"health",
"research"
],
[
"images",
"detection",
"scene",
"video",
"cnn",
"object",
"3d",
"image",
"visual",
"objects"
],
[
"self",
"features",
"deep",
"supervised",
"feature",
"domain",
"classification",
"dataset",
"explanations",
"attention"
],
[
"sequence",
"languages",
"translation",
"recurrent",
"text",
"language",
"attention",
"task",
"sentences",
"word"
],
[
"classification",
"metric",
"bias",
"class",
"label",
"prediction",
"labels",
"classifier",
"machine",
"generalization"
],
[
"speech",
"asr",
"music",
"speaker",
"acoustic",
"audio",
"recognition",
"signal",
"enhancement",
"quality"
],
[
"distributions",
"inference",
"posterior",
"bayesian",
"distribution",
"variational",
"gaussian",
"estimation",
"processes",
"probabilistic"
],
[
"pruning",
"hardware",
"architectures",
"networks",
"nas",
"accuracy",
"neural",
"memory",
"architecture",
"layers"
],
[
"robustness",
"attacks",
"adversarial",
"attack",
"perturbations",
"against",
"examples",
"defense",
"robust",
"attacker"
],
[
"environment",
"reward",
"control",
"agent",
"agents",
"action",
"policy",
"rl",
"reinforcement",
"policies"
]
] | 4.033339 | all-MiniLM-L6-v2 | 0.9 | 0.016575 | 0.153815 | 0.857305 |
End of preview. Expand
in Dataset Viewer.
README.md exists but content is empty.
Use the Edit dataset card button to edit it.
- Downloads last month
- 68