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FedGlu: A personalized federated learning-based glucose forecasting
algorithm for improved performance in glycemic excursion regions | http://arxiv.org/abs/2408.13926v1 | http://arxiv.org/abs/2408.13926v1 | http://arxiv.org/pdf/2408.13926v1 | 2024-08-25 | 2024-08-25 | [
"Darpit Dave",
"Kathan Vyas",
"Jagadish Kumaran Jayagopal",
"Alfredo Garcia",
"Madhav Erraguntla",
"Mark Lawley"
] | [
"",
"",
"",
"",
"",
""
] | Continuous glucose monitoring (CGM) devices provide real-time glucose
monitoring and timely alerts for glycemic excursions, improving glycemic
control among patients with diabetes. However, identifying rare events like
hypoglycemia and hyperglycemia remain challenging due to their infrequency.
Moreover, limited access to sensitive patient data hampers the development of
robust machine learning models. Our objective is to accurately predict glycemic
excursions while addressing data privacy concerns. To tackle excursion
prediction, we propose a novel Hypo-Hyper (HH) loss function, which
significantly improves performance in the glycemic excursion regions. The HH
loss function demonstrates a 46% improvement over mean-squared error (MSE) loss
across 125 patients. To address privacy concerns, we propose FedGlu, a machine
learning model trained in a federated learning (FL) framework. FL allows
collaborative learning without sharing sensitive data by training models
locally and sharing only model parameters across other patients. FedGlu
achieves a 35% superior glycemic excursion detection rate compared to local
models. This improvement translates to enhanced performance in predicting both,
hypoglycemia and hyperglycemia, for 105 out of 125 patients. These results
underscore the effectiveness of the proposed HH loss function in augmenting the
predictive capabilities of glucose predictions. Moreover, implementing models
within a federated learning framework not only ensures better predictive
capabilities but also safeguards sensitive data concurrently. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
Geo-Llama: Leveraging LLMs for Human Mobility Trajectory Generation with
Spatiotemporal Constraints | http://arxiv.org/abs/2408.13918v2 | http://arxiv.org/abs/2408.13918v2 | http://arxiv.org/pdf/2408.13918v2 | 2024-08-25 | 2024-08-28 | [
"Siyu Li",
"Toan Tran",
"Haowen Lin",
"John Krumm",
"Cyrus Shahabi",
"Li Xiong"
] | [
"",
"",
"",
"",
"",
""
] | Simulating human mobility data is essential for various application domains,
including transportation, urban planning, and epidemic control, since real data
are often inaccessible to researchers due to expensive costs and privacy
issues. Several existing deep generative solutions propose learning from real
trajectories to generate synthetic ones. Despite the progress, most of them
suffer from training stability issues and scale poorly with growing data size.
More importantly, they generally lack control mechanisms to steer the generated
trajectories based on spatiotemporal constraints such as fixing specific
visits. To address such limitations, we formally define the controlled
trajectory generation problem with spatiotemporal constraints and propose
Geo-Llama. This novel LLM-inspired framework enforces explicit visit
constraints in a contextually coherent way. It fine-tunes pre-trained LLMs on
trajectories with a visit-wise permutation strategy where each visit
corresponds to a time and location. This enables the model to capture the
spatiotemporal patterns regardless of visit orders and allows flexible and
in-context constraint integration through prompts during generation. Extensive
experiments on real-world and synthetic datasets validate the effectiveness of
Geo-Llama, demonstrating its versatility and robustness in handling a broad
range of constraints to generate more realistic trajectories compared to
existing methods. | cs.AI | [
"cs.AI"
] |
|||
LLMs are Superior Feedback Providers: Bootstrapping Reasoning for Lie
Detection with Self-Generated Feedback | http://arxiv.org/abs/2408.13915v1 | http://arxiv.org/abs/2408.13915v1 | http://arxiv.org/pdf/2408.13915v1 | 2024-08-25 | 2024-08-25 | [
"Tanushree Banerjee",
"Richard Zhu",
"Runzhe Yang",
"Karthik Narasimhan"
] | [
"",
"",
"",
""
] | Large Language Models (LLMs) excel at generating human-like dialogues and
comprehending text. However, understanding the subtleties of complex exchanges
in language remains a challenge. We propose a bootstrapping framework that
leverages self-generated feedback to enhance LLM reasoning capabilities for lie
detection. The framework consists of three stages: suggestion, feedback
collection, and modification. In the suggestion stage, a cost-effective
language model generates initial predictions based on game state and dialogue.
The feedback-collection stage involves a language model providing feedback on
these predictions. In the modification stage, a more advanced language model
refines the initial predictions using the auto-generated feedback. We
investigate the application of the proposed framework for detecting betrayal
and deception in Diplomacy games, and compare it with feedback from
professional human players. The LLM-generated feedback exhibits superior
quality and significantly enhances the performance of the model. Our approach
achieves a 39% improvement over the zero-shot baseline in lying-F1 without the
need for any training data, rivaling state-of-the-art supervised learning
results. | 19 pages, 18 figures | cs.CL | [
"cs.CL",
"cs.AI"
] |
||
ConVis: Contrastive Decoding with Hallucination Visualization for
Mitigating Hallucinations in Multimodal Large Language Models | http://arxiv.org/abs/2408.13906v1 | http://arxiv.org/abs/2408.13906v1 | http://arxiv.org/pdf/2408.13906v1 | 2024-08-25 | 2024-08-25 | [
"Yeji Park",
"Deokyeong Lee",
"Junsuk Choe",
"Buru Chang"
] | [
"",
"",
"",
""
] | Hallucinations in Multimodal Large Language Models (MLLMs) where generated
responses fail to accurately reflect the given image pose a significant
challenge to their reliability. To address this, we introduce ConVis, a novel
training-free contrastive decoding method. ConVis leverages a text-to-image
(T2I) generation model to semantically reconstruct the given image from
hallucinated captions. By comparing the contrasting probability distributions
produced by the original and reconstructed images, ConVis enables MLLMs to
capture visual contrastive signals that penalize hallucination generation.
Notably, this method operates purely within the decoding process, eliminating
the need for additional data or model updates. Our extensive experiments on
five popular benchmarks demonstrate that ConVis effectively reduces
hallucinations across various MLLMs, highlighting its potential to enhance
model reliability. | First two authors contributed equally. Source code is available at
https://github.com/yejipark-m/ConVis | cs.CV | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
||
SPICED: Syntactical Bug and Trojan Pattern Identification in A/MS
Circuits using LLM-Enhanced Detection | http://arxiv.org/abs/2408.16018v1 | http://arxiv.org/abs/2408.16018v1 | http://arxiv.org/pdf/2408.16018v1 | 2024-08-25 | 2024-08-25 | [
"Jayeeta Chaudhuri",
"Dhruv Thapar",
"Arjun Chaudhuri",
"Farshad Firouzi",
"Krishnendu Chakrabarty"
] | [
"",
"",
"",
"",
""
] | Analog and mixed-signal (A/MS) integrated circuits (ICs) are crucial in
modern electronics, playing key roles in signal processing, amplification,
sensing, and power management. Many IC companies outsource manufacturing to
third-party foundries, creating security risks such as stealthy analog Trojans.
Traditional detection methods, including embedding circuit watermarks or
conducting hardware-based monitoring, often impose significant area and power
overheads, and may not effectively identify all types of Trojans. To address
these shortcomings, we propose SPICED, a Large Language Model (LLM)-based
framework that operates within the software domain, eliminating the need for
hardware modifications for Trojan detection and localization. This is the first
work using LLM-aided techniques for detecting and localizing syntactical bugs
and analog Trojans in circuit netlists, requiring no explicit training and
incurring zero area overhead. Our framework employs chain-of-thought reasoning
and few-shot examples to teach anomaly detection rules to LLMs. With the
proposed method, we achieve an average Trojan coverage of 93.32% and an average
true positive rate of 93.4% in identifying Trojan-impacted nodes for the
evaluated analog benchmark circuits. These experimental results validate the
effectiveness of LLMs in detecting and locating both syntactical bugs and
Trojans within analog netlists. | Accepted at PAINE'24 | cs.CR | [
"cs.CR",
"cs.AI",
"cs.LG"
] |
||
Enhancing SQL Query Generation with Neurosymbolic Reasoning | http://arxiv.org/abs/2408.13888v1 | http://arxiv.org/abs/2408.13888v1 | http://arxiv.org/pdf/2408.13888v1 | 2024-08-25 | 2024-08-25 | [
"Henrijs Princis",
"Cristina David",
"Alan Mycroft"
] | [
"",
"",
""
] | Neurosymbolic approaches blend the effectiveness of symbolic reasoning with
the flexibility of neural networks. In this work, we propose a neurosymbolic
architecture for generating SQL queries that builds and explores a solution
tree using Best-First Search, with the possibility of backtracking. For this
purpose, it integrates a Language Model (LM) with symbolic modules that help
catch and correct errors made by the LM on SQL queries, as well as guiding the
exploration of the solution tree. We focus on improving the performance of
smaller open-source LMs, and we find that our tool, Xander, increases accuracy
by an average of 10.9% and reduces runtime by an average of 28% compared to the
LM without Xander, enabling a smaller LM (with Xander) to outperform its
four-times larger counterpart (without Xander). | 11 pages, 8 figures | cs.DB | [
"cs.DB",
"cs.AI",
"cs.SE",
"I.2"
] |
||
Flexible game-playing AI with AlphaViT: adapting to multiple games and
board sizes | http://arxiv.org/abs/2408.13871v1 | http://arxiv.org/abs/2408.13871v1 | http://arxiv.org/pdf/2408.13871v1 | 2024-08-25 | 2024-08-25 | [
"Kazuhisa Fujita"
] | [
""
] | This paper presents novel game AI agents based on the AlphaZero framework,
enhanced with Vision Transformers (ViT): AlphaViT, AlphaViD, and AlphaVDA.
These agents are designed to play various board games of different sizes using
a single model, overcoming AlphaZero's limitation of being restricted to a
fixed board size. AlphaViT uses only a transformer encoder, while AlphaViD and
AlphaVDA contain both an encoder and a decoder. AlphaViD's decoder receives
input from the encoder output, while AlphaVDA uses a learnable matrix as
decoder input. Using the AlphaZero framework, the three proposed methods
demonstrate their versatility in different game environments, including
Connect4, Gomoku, and Othello. Experimental results show that these agents,
whether trained on a single game or on multiple games simultaneously,
consistently outperform traditional algorithms such as Minimax and Monte Carlo
tree search using a single DNN with shared weights, while approaching the
performance of AlphaZero. In particular, AlphaViT and AlphaViD show strong
performance across games, with AlphaViD benefiting from an additional decoder
layer that enhances its ability to adapt to different action spaces and board
sizes. These results may suggest the potential of transformer-based
architectures to develop more flexible and robust game AI agents capable of
excelling in multiple games and dynamic environments. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
CodeGraph: Enhancing Graph Reasoning of LLMs with Code | http://arxiv.org/abs/2408.13863v1 | http://arxiv.org/abs/2408.13863v1 | http://arxiv.org/pdf/2408.13863v1 | 2024-08-25 | 2024-08-25 | [
"Qiaolong Cai",
"Zhaowei Wang",
"Shizhe Diao",
"James Kwok",
"Yangqiu Song"
] | [
"",
"",
"",
"",
""
] | With the increasing popularity of large language models (LLMs), reasoning on
basic graph algorithm problems is an essential intermediate step in assessing
their abilities to process and infer complex graph reasoning tasks. Existing
methods usually convert graph-structured data to textual descriptions and then
use LLMs for reasoning and computation. However, LLMs often produce computation
errors on arithmetic parts in basic graph algorithm problems, such as counting
number of edges. In addition, they struggle to control or understand the output
of the reasoning process, raising concerns about whether LLMs are simply
guessing. In this paper, we introduce CodeGraph, a method that encodes graph
problem solutions as code. The methods solve new graph problems by learning
from exemplars, generating programs, and executing them via a program
interpreter. Using the few-shot setting, we evaluate CodeGraph with the base
LLM being GPT-3.5 Turbo, Llama3-70B Instruct, Mixtral-8x22B Instruct, and
Mixtral-8x7B Instruct. Experimental results on six tasks with six graph
encoding methods in the GraphQA dataset demonstrate that CodeGraph can boost
performance on graph reasoning tasks inside LLMs by 1.3% to 58.6%, depending on
the task. Compared to the existing methods, CodeGraph demonstrates strong
performance on arithmetic problems in graph tasks and offers a more
controllable and interpretable approach to the reasoning process. | In Progress | cs.CL | [
"cs.CL",
"cs.AI"
] |
||
Tangram: A Challenging Benchmark for Geometric Element Recognizing | http://arxiv.org/abs/2408.13854v1 | http://arxiv.org/abs/2408.13854v1 | http://arxiv.org/pdf/2408.13854v1 | 2024-08-25 | 2024-08-25 | [
"Jiamin Tang",
"Chao Zhang",
"Xudong Zhu",
"Mengchi Liu"
] | [
"",
"",
"",
""
] | Significant advancements in Large Multimodal Models (LMMs) have enabled them
to tackle complex problems involving visual-mathematical reasoning. However,
their ability to identify geometric elements remains understudied. To bridge
this gap, we introduce Tangram, a novel benchmark designed to evaluate the
performance of LMMs on geometric element recognition. Tangram includes 1,080
diverse geometric diagrams sourced from primary and secondary school exams,
competitions, and textbooks, covering from simple basic geometric shapes to
complex combinations. Each diagram is associated with four questions, resulting
in a total of 4,320 visual-question-answer pairs. Unlike existing benchmarks
that seek higher-level cognition and reasoning, Tangram focuses on the
understanding of geometric elements, requiring models to perform a "simple but
interesting" counting task. Systematic evaluation of 10 prominent LMMs, such as
GPT-4o and Claude 3.5 Sonnet, shows that even in the seemingly simple task,
these models still face significant challenges. Notably, the overall accuracy
of the top performer across all tested models is only 56.8%, marking a
significant gap when compared to human performance. These findings highlight
the limitations of current multimodal artificial intelligence systems in
handling basic perception tasks, and will inspire the development of the next
generation of expert-level multimodal foundational models. The Tangram and
evaluation code will be available soon. | 12 pages, 7 figures | cs.CV | [
"cs.CV",
"cs.AI"
] |
||
Condensed Sample-Guided Model Inversion for Knowledge Distillation | http://arxiv.org/abs/2408.13850v1 | http://arxiv.org/abs/2408.13850v1 | http://arxiv.org/pdf/2408.13850v1 | 2024-08-25 | 2024-08-25 | [
"Kuluhan Binici",
"Shivam Aggarwal",
"Cihan Acar",
"Nam Trung Pham",
"Karianto Leman",
"Gim Hee Lee",
"Tulika Mitra"
] | [
"",
"",
"",
"",
"",
"",
""
] | Knowledge distillation (KD) is a key element in neural network compression
that allows knowledge transfer from a pre-trained teacher model to a more
compact student model. KD relies on access to the training dataset, which may
not always be fully available due to privacy concerns or logistical issues
related to the size of the data. To address this, "data-free" KD methods use
synthetic data, generated through model inversion, to mimic the target data
distribution. However, conventional model inversion methods are not designed to
utilize supplementary information from the target dataset, and thus, cannot
leverage it to improve performance, even when it is available. In this paper,
we consider condensed samples, as a form of supplementary information, and
introduce a method for using them to better approximate the target data
distribution, thereby enhancing the KD performance. Our approach is versatile,
evidenced by improvements of up to 11.4% in KD accuracy across various datasets
and model inversion-based methods. Importantly, it remains effective even when
using as few as one condensed sample per class, and can also enhance
performance in few-shot scenarios where only limited real data samples are
available. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
PropSAM: A Propagation-Based Model for Segmenting Any 3D Objects in
Multi-Modal Medical Images | http://arxiv.org/abs/2408.13836v1 | http://arxiv.org/abs/2408.13836v1 | http://arxiv.org/pdf/2408.13836v1 | 2024-08-25 | 2024-08-25 | [
"Zifan Chen",
"Xinyu Nan",
"Jiazheng Li",
"Jie Zhao",
"Haifeng Li",
"Zilin Lin",
"Haoshen Li",
"Heyun Chen",
"Yiting Liu",
"Bin Dong",
"Li Zhang",
"Lei Tang"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
""
] | Volumetric segmentation is crucial for medical imaging but is often
constrained by labor-intensive manual annotations and the need for
scenario-specific model training. Furthermore, existing general segmentation
models are inefficient due to their design and inferential approaches.
Addressing this clinical demand, we introduce PropSAM, a propagation-based
segmentation model that optimizes the use of 3D medical structure information.
PropSAM integrates a CNN-based UNet for intra-slice processing with a
Transformer-based module for inter-slice propagation, focusing on structural
and semantic continuities to enhance segmentation across various modalities.
Distinctively, PropSAM operates on a one-view prompt, such as a 2D bounding box
or sketch mask, unlike conventional models that require two-view prompts. It
has demonstrated superior performance, significantly improving the Dice
Similarity Coefficient (DSC) across 44 medical datasets and various imaging
modalities, outperforming models like MedSAM and SegVol with an average DSC
improvement of 18.1%. PropSAM also maintains stable predictions despite prompt
deviations and varying propagation configurations, confirmed by one-way ANOVA
tests with P>0.5985 and P>0.6131, respectively. Moreover, PropSAM's efficient
architecture enables faster inference speeds (Wilcoxon rank-sum test, P<0.001)
and reduces user interaction time by 37.8% compared to two-view prompt models.
Its ability to handle irregular and complex objects with robust performance
further demonstrates its potential in clinical settings, facilitating more
automated and reliable medical imaging analyses with minimal retraining. | 26 figures, 6 figures | cs.CV | [
"cs.CV",
"cs.AI"
] |
||
Guardians of the Machine Translation Meta-Evaluation: Sentinel Metrics
Fall In! | http://arxiv.org/abs/2408.13831v1 | http://arxiv.org/abs/2408.13831v1 | http://arxiv.org/pdf/2408.13831v1 | 2024-08-25 | 2024-08-25 | [
"Stefano Perrella",
"Lorenzo Proietti",
"Alessandro Scirè",
"Edoardo Barba",
"Roberto Navigli"
] | [
"",
"",
"",
"",
""
] | Annually, at the Conference of Machine Translation (WMT), the Metrics Shared
Task organizers conduct the meta-evaluation of Machine Translation (MT)
metrics, ranking them according to their correlation with human judgments.
Their results guide researchers toward enhancing the next generation of metrics
and MT systems. With the recent introduction of neural metrics, the field has
witnessed notable advancements. Nevertheless, the inherent opacity of these
metrics has posed substantial challenges to the meta-evaluation process. This
work highlights two issues with the meta-evaluation framework currently
employed in WMT, and assesses their impact on the metrics rankings. To do this,
we introduce the concept of sentinel metrics, which are designed explicitly to
scrutinize the meta-evaluation process's accuracy, robustness, and fairness. By
employing sentinel metrics, we aim to validate our findings, and shed light on
and monitor the potential biases or inconsistencies in the rankings. We
discover that the present meta-evaluation framework favors two categories of
metrics: i) those explicitly trained to mimic human quality assessments, and
ii) continuous metrics. Finally, we raise concerns regarding the evaluation
capabilities of state-of-the-art metrics, emphasizing that they might be basing
their assessments on spurious correlations found in their training data. | Presented at ACL 2024 Main Conference. 29 pages | cs.CL | [
"cs.CL",
"cs.AI"
] |
||
RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in
Node-Classification | http://arxiv.org/abs/2408.13825v1 | http://arxiv.org/abs/2408.13825v1 | http://arxiv.org/pdf/2408.13825v1 | 2024-08-25 | 2024-08-25 | [
"S. Akansha"
] | [
""
] | Graph Neural Networks (GNNs) have emerged as powerful tools for predicting
outcomes in graph-structured data. However, a notable limitation of GNNs is
their inability to provide robust uncertainty estimates, which undermines their
reliability in contexts where errors are costly. One way to address this issue
is by providing prediction sets that contain the true label with a predefined
probability margin. Our approach builds upon conformal prediction (CP), a
framework that promises to construct statistically robust prediction sets or
intervals. There are two primary challenges: first, given dependent data like
graphs, it is unclear whether the critical assumption in CP - exchangeability -
still holds when applied to node classification. Second, even if the
exchangeability assumption is valid for conformalized link prediction, we need
to ensure high efficiency, i.e., the resulting prediction set or the interval
length is small enough to provide useful information. In this article, we
propose a novel approach termed Robust Conformal Prediction for GNNs
(RoCP-GNN), which integrates conformal prediction (CP) directly into the GNN
training process. This method generates prediction sets, instead of just point
predictions, that are valid at a user-defined confidence level, assuming only
exchangeability. Our approach robustly predicts outcomes with any predictive
GNN model while quantifying the uncertainty in predictions within the realm of
graph-based semi-supervised learning (SSL). Experimental results demonstrate
that GNN models with size loss provide a statistically significant increase in
performance. We validate our approach on standard graph benchmark datasets by
coupling it with various state-of-the-art GNNs in node classification. The code
will be made available after publication. | 12, 5 figures | cs.LG | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
||
A Joint Learning Model with Variational Interaction for Multilingual
Program Translation | http://arxiv.org/abs/2408.14515v1 | http://arxiv.org/abs/2408.14515v1 | http://arxiv.org/pdf/2408.14515v1 | 2024-08-25 | 2024-08-25 | [
"Yali Du",
"Hui Sun",
"Ming Li"
] | [
"",
"",
""
] | Programs implemented in various programming languages form the foundation of
software applications. To alleviate the burden of program migration and
facilitate the development of software systems, automated program translation
across languages has garnered significant attention. Previous approaches
primarily focus on pairwise translation paradigms, learning translation between
pairs of languages using bilingual parallel data. However, parallel data is
difficult to collect for some language pairs, and the distribution of program
semantics across languages can shift, posing challenges for pairwise program
translation. In this paper, we argue that jointly learning a unified model to
translate code across multiple programming languages is superior to separately
learning from bilingual parallel data. We propose Variational Interaction for
Multilingual Program Translation~(VIM-PT), a disentanglement-based generative
approach that jointly trains a unified model for multilingual program
translation across multiple languages. VIM-PT disentangles code into
language-shared and language-specific features, using variational inference and
interaction information with a novel lower bound, then achieves program
translation through conditional generation. VIM-PT demonstrates four
advantages: 1) captures language-shared information more accurately from
various implementations and improves the quality of multilingual program
translation, 2) mines and leverages the capability of non-parallel data, 3)
addresses the distribution shift of program semantics across languages, 4) and
serves as a unified model, reducing deployment complexity. | Accepted by the 39th IEEE/ACM International Conference on Automated
Software Engineering (ASE 2024) | cs.SE | [
"cs.SE",
"cs.AI",
"cs.LG",
"cs.PL"
] |
||
Localization of Synthetic Manipulations in Western Blot Images | http://arxiv.org/abs/2408.13786v1 | http://arxiv.org/abs/2408.13786v1 | http://arxiv.org/pdf/2408.13786v1 | 2024-08-25 | 2024-08-25 | [
"Anmol Manjunath",
"Viola Negroni",
"Sara Mandelli",
"Daniel Moreira",
"Paolo Bestagini"
] | [
"",
"",
"",
"",
""
] | Recent breakthroughs in deep learning and generative systems have
significantly fostered the creation of synthetic media, as well as the local
alteration of real content via the insertion of highly realistic synthetic
manipulations. Local image manipulation, in particular, poses serious
challenges to the integrity of digital content and societal trust. This problem
is not only confined to multimedia data, but also extends to biological images
included in scientific publications, like images depicting Western blots. In
this work, we address the task of localizing synthetic manipulations in Western
blot images. To discriminate between pristine and synthetic pixels of an
analyzed image, we propose a synthetic detector that operates on small patches
extracted from the image. We aggregate patch contributions to estimate a
tampering heatmap, highlighting synthetic pixels out of pristine ones. Our
methodology proves effective when tested over two manipulated Western blot
image datasets, one altered automatically and the other manually by exploiting
advanced AI-based image manipulation tools that are unknown at our training
stage. We also explore the robustness of our method over an external dataset of
other scientific images depicting different semantics, manipulated through
unseen generation techniques. | cs.CV | [
"cs.CV",
"cs.AI",
"cs.MM"
] |
|||
Analyzing the Impact of Splicing Artifacts in Partially Fake Speech
Signals | http://arxiv.org/abs/2408.13784v1 | http://arxiv.org/abs/2408.13784v1 | http://arxiv.org/pdf/2408.13784v1 | 2024-08-25 | 2024-08-25 | [
"Viola Negroni",
"Davide Salvi",
"Paolo Bestagini",
"Stefano Tubaro"
] | [
"",
"",
"",
""
] | Speech deepfake detection has recently gained significant attention within
the multimedia forensics community. Related issues have also been explored,
such as the identification of partially fake signals, i.e., tracks that include
both real and fake speech segments. However, generating high-quality spliced
audio is not as straightforward as it may appear. Spliced signals are typically
created through basic signal concatenation. This process could introduce
noticeable artifacts that can make the generated data easier to detect. We
analyze spliced audio tracks resulting from signal concatenation, investigate
their artifacts and assess whether such artifacts introduce any bias in
existing datasets. Our findings reveal that by analyzing splicing artifacts, we
can achieve a detection EER of 6.16% and 7.36% on PartialSpoof and HAD
datasets, respectively, without needing to train any detector. These results
underscore the complexities of generating reliable spliced audio data and lead
to discussions that can help improve future research in this area. | Accepted at ASVspoof 5 Workshop (Interspeech2024 Satellite) | cs.SD | [
"cs.SD",
"cs.AI",
"cs.MM",
"eess.AS"
] |
||
Variational autoencoder-based neural network model compression | http://arxiv.org/abs/2408.14513v1 | http://arxiv.org/abs/2408.14513v1 | http://arxiv.org/pdf/2408.14513v1 | 2024-08-25 | 2024-08-25 | [
"Liang Cheng",
"Peiyuan Guan",
"Amir Taherkordi",
"Lei Liu",
"Dapeng Lan"
] | [
"",
"",
"",
"",
""
] | Variational Autoencoders (VAEs), as a form of deep generative model, have
been widely used in recent years, and shown great great peformance in a number
of different domains, including image generation and anomaly detection, etc..
This paper aims to explore neural network model compression method based on
VAE. The experiment uses different neural network models for MNIST recognition
as compression targets, including Feedforward Neural Network (FNN),
Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long
Short-Term Memory (LSTM). These models are the most basic models in deep
learning, and other more complex and advanced models are based on them or
inherit their features and evolve. In the experiment, the first step is to
train the models mentioned above, each trained model will have different
accuracy and number of total parameters. And then the variants of parameters
for each model are processed as training data in VAEs separately, and the
trained VAEs are tested by the true model parameters. The experimental results
show that using the latent space as a representation of the model compression
can improve the compression rate compared to some traditional methods such as
pruning and quantization, meanwhile the accuracy is not greatly affected using
the model parameters reconstructed based on the latent space. In the future, a
variety of different large-scale deep learning models will be used more widely,
so exploring different ways to save time and space on saving or transferring
models will become necessary, and the use of VAE in this paper can provide a
basis for these further explorations. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
SAB:A Stealing and Robust Backdoor Attack based on Steganographic
Algorithm against Federated Learning | http://arxiv.org/abs/2408.13773v1 | http://arxiv.org/abs/2408.13773v1 | http://arxiv.org/pdf/2408.13773v1 | 2024-08-25 | 2024-08-25 | [
"Weida Xu",
"Yang Xu",
"Sicong Zhang"
] | [
"",
"",
""
] | Federated learning, an innovative network architecture designed to safeguard
user privacy, is gaining widespread adoption in the realm of technology.
However, given the existence of backdoor attacks in federated learning,
exploring the security of federated learning is significance. Nevertheless, the
backdoors investigated in current federated learning research can be readily
detected by human inspection or resisted by detection algorithms. Accordingly,
a new goal has been set to develop stealing and robust federated learning
backdoor attacks. In this paper, we introduce a novel approach, SAB, tailored
specifically for backdoor attacks in federated learning, presenting an
alternative gradient updating mechanism. SAB attack based on steganographic
algorithm, using image steganographic algorithm to build a full-size trigger to
improve the accuracy of backdoors and use multiple loss joint computation to
produce triggers. SAB exhibits smaller distances to benign samples and greater
imperceptibility to the human eye. As such, our triggers are capable of
mitigating or evading specific backdoor defense methods. In SAB, the
bottom-95\% method is applied to extend the lifespan of backdoor attacks. It
updates the gradient on minor value points to reduce the probability of being
cleaned. Finally, the generalization of backdoors is enhanced with
Sparse-update to improve the backdoor accuracy. | cs.CR | [
"cs.CR",
"cs.AI"
] |
|||
Lecture Notes on Linear Neural Networks: A Tale of Optimization and
Generalization in Deep Learning | http://arxiv.org/abs/2408.13767v1 | http://arxiv.org/abs/2408.13767v1 | http://arxiv.org/pdf/2408.13767v1 | 2024-08-25 | 2024-08-25 | [
"Nadav Cohen",
"Noam Razin"
] | [
"",
""
] | These notes are based on a lecture delivered by NC on March 2021, as part of
an advanced course in Princeton University on the mathematical understanding of
deep learning. They present a theory (developed by NC, NR and collaborators) of
linear neural networks -- a fundamental model in the study of optimization and
generalization in deep learning. Practical applications born from the presented
theory are also discussed. The theory is based on mathematical tools that are
dynamical in nature. It showcases the potential of such tools to push the
envelope of our understanding of optimization and generalization in deep
learning. The text assumes familiarity with the basics of statistical learning
theory. Exercises (without solutions) are included. | Lecture notes | cs.LG | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
||
Multimodal Ensemble with Conditional Feature Fusion for Dysgraphia
Diagnosis in Children from Handwriting Samples | http://arxiv.org/abs/2408.13754v1 | http://arxiv.org/abs/2408.13754v1 | http://arxiv.org/pdf/2408.13754v1 | 2024-08-25 | 2024-08-25 | [
"Jayakanth Kunhoth",
"Somaya Al-Maadeed",
"Moutaz Saleh",
"Younes Akbari"
] | [
"",
"",
"",
""
] | Developmental dysgraphia is a neurological disorder that hinders children's
writing skills. In recent years, researchers have increasingly explored machine
learning methods to support the diagnosis of dysgraphia based on offline and
online handwriting. In most previous studies, the two types of handwriting have
been analysed separately, which does not necessarily lead to promising results.
In this way, the relationship between online and offline data cannot be
explored. To address this limitation, we propose a novel multimodal machine
learning approach utilizing both online and offline handwriting data. We
created a new dataset by transforming an existing online handwritten dataset,
generating corresponding offline handwriting images. We considered only
different types of word data (simple word, pseudoword & difficult word) in our
multimodal analysis. We trained SVM and XGBoost classifiers separately on
online and offline features as well as implemented multimodal feature fusion
and soft-voted ensemble. Furthermore, we proposed a novel ensemble with
conditional feature fusion method which intelligently combines predictions from
online and offline classifiers, selectively incorporating feature fusion when
confidence scores fall below a threshold. Our novel approach achieves an
accuracy of 88.8%, outperforming SVMs for single modalities by 12-14%, existing
methods by 8-9%, and traditional multimodal approaches (soft-vote ensemble and
feature fusion) by 3% and 5%, respectively. Our methodology contributes to the
development of accurate and efficient dysgraphia diagnosis tools, requiring
only a single instance of multimodal word/pseudoword data to determine the
handwriting impairment. This work highlights the potential of multimodal
learning in enhancing dysgraphia diagnosis, paving the way for accessible and
practical diagnostic tools. | cs.CV | [
"cs.CV",
"cs.AI",
"I.2.6; I.2.10; I.4.9; I.5.1; I.5.4"
] |
|||
Multi-Agent Target Assignment and Path Finding for Intelligent
Warehouse: A Cooperative Multi-Agent Deep Reinforcement Learning Perspective | http://arxiv.org/abs/2408.13750v1 | http://arxiv.org/abs/2408.13750v1 | http://arxiv.org/pdf/2408.13750v1 | 2024-08-25 | 2024-08-25 | [
"Qi Liu",
"Jianqi Gao",
"Dongjie Zhu",
"Xizheng Pang",
"Pengbin Chen",
"Jingxiang Guo",
"Yanjie Li"
] | [
"",
"",
"",
"",
"",
"",
""
] | Multi-agent target assignment and path planning (TAPF) are two key problems
in intelligent warehouse. However, most literature only addresses one of these
two problems separately. In this study, we propose a method to simultaneously
solve target assignment and path planning from a perspective of cooperative
multi-agent deep reinforcement learning (RL). To the best of our knowledge,
this is the first work to model the TAPF problem for intelligent warehouse to
cooperative multi-agent deep RL, and the first to simultaneously address TAPF
based on multi-agent deep RL. Furthermore, previous literature rarely considers
the physical dynamics of agents. In this study, the physical dynamics of the
agents is considered. Experimental results show that our method performs well
in various task settings, which means that the target assignment is solved
reasonably well and the planned path is almost shortest. Moreover, our method
is more time-efficient than baselines. | cs.AI | [
"cs.AI",
"cs.MA"
] |
|||
DOCE: Finding the Sweet Spot for Execution-Based Code Generation | http://arxiv.org/abs/2408.13745v1 | http://arxiv.org/abs/2408.13745v1 | http://arxiv.org/pdf/2408.13745v1 | 2024-08-25 | 2024-08-25 | [
"Haau-Sing Li",
"Patrick Fernandes",
"Iryna Gurevych",
"André F. T. Martins"
] | [
"",
"",
"",
""
] | Recently, a diverse set of decoding and reranking procedures have been shown
effective for LLM-based code generation. However, a comprehensive framework
that links and experimentally compares these methods is missing. We address
this by proposing Decoding Objectives for Code Execution, a comprehensive
framework that includes candidate generation, $n$-best reranking, minimum Bayes
risk (MBR) decoding, and self-debugging as the core components. We then study
the contributions of these components through execution-based evaluation
metrics. Our findings highlight the importance of execution-based methods and
the difference gap between execution-based and execution-free methods.
Furthermore, we assess the impact of filtering based on trial unit tests, a
simple and effective strategy that has been often overlooked in prior works. We
also propose self-debugging on multiple candidates, obtaining state-of-the-art
performance on reranking for code generation. We expect our framework to
provide a solid guideline for future research on code generation. | 10 pages (32 including appendix), 5 figures, 25 tables. arXiv admin
note: text overlap with arXiv:2304.05128 by other authors | cs.CL | [
"cs.CL",
"cs.AI",
"cs.PL"
] |
||
LogParser-LLM: Advancing Efficient Log Parsing with Large Language
Models | http://arxiv.org/abs/2408.13727v1 | http://arxiv.org/abs/2408.13727v1 | http://arxiv.org/pdf/2408.13727v1 | 2024-08-25 | 2024-08-25 | [
"Aoxiao Zhong",
"Dengyao Mo",
"Guiyang Liu",
"Jinbu Liu",
"Qingda Lu",
"Qi Zhou",
"Jiesheng Wu",
"Quanzheng Li",
"Qingsong Wen"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
""
] | Logs are ubiquitous digital footprints, playing an indispensable role in
system diagnostics, security analysis, and performance optimization. The
extraction of actionable insights from logs is critically dependent on the log
parsing process, which converts raw logs into structured formats for downstream
analysis. Yet, the complexities of contemporary systems and the dynamic nature
of logs pose significant challenges to existing automatic parsing techniques.
The emergence of Large Language Models (LLM) offers new horizons. With their
expansive knowledge and contextual prowess, LLMs have been transformative
across diverse applications. Building on this, we introduce LogParser-LLM, a
novel log parser integrated with LLM capabilities. This union seamlessly blends
semantic insights with statistical nuances, obviating the need for
hyper-parameter tuning and labeled training data, while ensuring rapid
adaptability through online parsing. Further deepening our exploration, we
address the intricate challenge of parsing granularity, proposing a new metric
and integrating human interactions to allow users to calibrate granularity to
their specific needs. Our method's efficacy is empirically demonstrated through
evaluations on the Loghub-2k and the large-scale LogPub benchmark. In
evaluations on the LogPub benchmark, involving an average of 3.6 million logs
per dataset across 14 datasets, our LogParser-LLM requires only 272.5 LLM
invocations on average, achieving a 90.6% F1 score for grouping accuracy and an
81.1% for parsing accuracy. These results demonstrate the method's high
efficiency and accuracy, outperforming current state-of-the-art log parsers,
including pattern-based, neural network-based, and existing LLM-enhanced
approaches. | Accepted by ACM KDD 2024 | cs.SE | [
"cs.SE",
"cs.AI"
] |
||
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with
LLM Token Embeddings | http://arxiv.org/abs/2408.14512v1 | http://arxiv.org/abs/2408.14512v1 | http://arxiv.org/pdf/2408.14512v1 | 2024-08-25 | 2024-08-25 | [
"Duo Wang",
"Yuan Zuo",
"Fengzhi Li",
"Junjie Wu"
] | [
"",
"",
"",
""
] | Zero-shot graph machine learning, especially with graph neural networks
(GNNs), has garnered significant interest due to the challenge of scarce
labeled data. While methods like self-supervised learning and graph prompt
learning have been extensively explored, they often rely on fine-tuning with
task-specific labels, limiting their effectiveness in zero-shot scenarios.
Inspired by the zero-shot capabilities of instruction-fine-tuned large language
models (LLMs), we introduce a novel framework named Token Embedding-Aligned
Graph Language Model (TEA-GLM) that leverages LLMs as cross-dataset and
cross-task zero-shot learners for graph machine learning. Concretely, we
pretrain a GNN, aligning its representations with token embeddings of an LLM.
We then train a linear projector that transforms the GNN's representations into
a fixed number of graph token embeddings without tuning the LLM. A unified
instruction is designed for various graph tasks at different levels, such as
node classification (node-level) and link prediction (edge-level). These design
choices collectively enhance our method's effectiveness in zero-shot learning,
setting it apart from existing methods. Experiments show that our graph token
embeddings help the LLM predictor achieve state-of-the-art performance on
unseen datasets and tasks compared to other methods using LLMs as predictors. | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CL"
] |
|||
Count-based Novelty Exploration in Classical Planning | http://arxiv.org/abs/2408.13719v1 | http://arxiv.org/abs/2408.13719v1 | http://arxiv.org/pdf/2408.13719v1 | 2024-08-25 | 2024-08-25 | [
"Giacomo Rosa",
"Nir Lipovetzky"
] | [
"",
""
] | Count-based exploration methods are widely employed to improve the
exploratory behavior of learning agents over sequential decision problems.
Meanwhile, Novelty search has achieved success in Classical Planning through
recording of the first, but not successive, occurrences of tuples. In order to
structure the exploration, however, the number of tuples considered needs to
grow exponentially as the search progresses. We propose a new novelty
technique, classical count-based novelty, which aims to explore the state space
with a constant number of tuples, by leveraging the frequency of each tuple's
appearance in a search tree. We then justify the mechanisms through which lower
tuple counts lead the search towards novel tuples. We also introduce
algorithmic contributions in the form of a trimmed open list that maintains a
constant size by pruning nodes with bad novelty values. These techniques are
shown to complement existing novelty heuristics when integrated in a classical
solver, achieving competitive results in challenging benchmarks from recent
International Planning Competitions. Moreover, adapting our solver as the
frontend planner in dual configurations that utilize both memory and time
thresholds demonstrates a significant increase in instance coverage, surpassing
current state-of-the-art solvers. | Extended version of paper accepted for publication at ECAI 2024 | cs.AI | [
"cs.AI"
] |
||
Unveiling the Statistical Foundations of Chain-of-Thought Prompting
Methods | http://arxiv.org/abs/2408.14511v2 | http://arxiv.org/abs/2408.14511v2 | http://arxiv.org/pdf/2408.14511v2 | 2024-08-25 | 2024-08-28 | [
"Xinyang Hu",
"Fengzhuo Zhang",
"Siyu Chen",
"Zhuoran Yang"
] | [
"",
"",
"",
""
] | Chain-of-Thought (CoT) prompting and its variants have gained popularity as
effective methods for solving multi-step reasoning problems using pretrained
large language models (LLMs). In this work, we analyze CoT prompting from a
statistical estimation perspective, providing a comprehensive characterization
of its sample complexity. To this end, we introduce a multi-step latent
variable model that encapsulates the reasoning process, where the latent
variable encodes the task information. Under this framework, we demonstrate
that when the pretraining dataset is sufficiently large, the estimator formed
by CoT prompting is equivalent to a Bayesian estimator. This estimator
effectively solves the multi-step reasoning problem by aggregating a posterior
distribution inferred from the demonstration examples in the prompt. Moreover,
we prove that the statistical error of the CoT estimator can be decomposed into
two main components: (i) a prompting error, which arises from inferring the
true task using CoT prompts, and (ii) the statistical error of the pretrained
LLM. We establish that, under appropriate assumptions, the prompting error
decays exponentially to zero as the number of demonstrations increases.
Additionally, we explicitly characterize the approximation and generalization
errors of the pretrained LLM. Notably, we construct a transformer model that
approximates the target distribution of the multi-step reasoning problem with
an error that decreases exponentially in the number of transformer blocks. Our
analysis extends to other variants of CoT, including Self-Consistent CoT,
Tree-of-Thought, and Selection-Inference, offering a broad perspective on the
efficacy of these methods. We also provide numerical experiments to validate
the theoretical findings. | 150 pages, 18 figures, 3 tables | cs.AI | [
"cs.AI",
"cs.CL",
"cs.LG",
"math.ST",
"stat.ML",
"stat.TH"
] |
||
DHP Benchmark: Are LLMs Good NLG Evaluators? | http://arxiv.org/abs/2408.13704v1 | http://arxiv.org/abs/2408.13704v1 | http://arxiv.org/pdf/2408.13704v1 | 2024-08-25 | 2024-08-25 | [
"Yicheng Wang",
"Jiayi Yuan",
"Yu-Neng Chuang",
"Zhuoer Wang",
"Yingchi Liu",
"Mark Cusick",
"Param Kulkarni",
"Zhengping Ji",
"Yasser Ibrahim",
"Xia Hu"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
"",
""
] | Large Language Models (LLMs) are increasingly serving as evaluators in
Natural Language Generation (NLG) tasks. However, the capabilities of LLMs in
scoring NLG quality remain inadequately explored. Current studies depend on
human assessments and simple metrics that fail to capture the discernment of
LLMs across diverse NLG tasks. To address this gap, we propose the Discernment
of Hierarchical Perturbation (DHP) benchmarking framework, which provides
quantitative discernment scores for LLMs utilizing hierarchically perturbed
text data and statistical tests to measure the NLG evaluation capabilities of
LLMs systematically. We have re-established six evaluation datasets for this
benchmark, covering four NLG tasks: Summarization, Story Completion, Question
Answering, and Translation. Our comprehensive benchmarking of five major LLM
series provides critical insight into their strengths and limitations as NLG
evaluators. | cs.CL | [
"cs.CL",
"cs.AI"
] |
|||
Differentially Private Publication of Electricity Time Series Data in
Smart Grids | http://arxiv.org/abs/2408.16017v1 | http://arxiv.org/abs/2408.16017v1 | http://arxiv.org/pdf/2408.16017v1 | 2024-08-24 | 2024-08-24 | [
"Sina Shaham",
"Gabriel Ghinita",
"Bhaskar Krishnamachari",
"Cyrus Shahabi"
] | [
"",
"",
"",
""
] | Smart grids are a valuable data source to study consumer behavior and guide
energy policy decisions. In particular, time-series of power consumption over
geographical areas are essential in deciding the optimal placement of expensive
resources (e.g., transformers, storage elements) and their activation
schedules. However, publication of such data raises significant privacy issues,
as it may reveal sensitive details about personal habits and lifestyles.
Differential privacy (DP) is well-suited for sanitization of individual data,
but current DP techniques for time series lead to significant loss in utility,
due to the existence of temporal correlation between data readings. We
introduce {\em STPT (Spatio-Temporal Private Timeseries)}, a novel method for
DP-compliant publication of electricity consumption data that analyzes
spatio-temporal attributes and captures both micro and macro patterns by
leveraging RNNs. Additionally, it employs a partitioning method for releasing
electricity consumption time series based on identified patterns. We
demonstrate through extensive experiments, on both real-world and synthetic
datasets, that STPT significantly outperforms existing benchmarks, providing a
well-balanced trade-off between data utility and user privacy. | cs.CR | [
"cs.CR",
"cs.AI",
"cs.LG"
] |
|||
Evaluating Alternative Training Interventions Using Personalized
Computational Models of Learning | http://arxiv.org/abs/2408.13684v1 | http://arxiv.org/abs/2408.13684v1 | http://arxiv.org/pdf/2408.13684v1 | 2024-08-24 | 2024-08-24 | [
"Christopher James MacLellan",
"Kimberly Stowers",
"Lisa Brady"
] | [
"",
"",
""
] | Evaluating different training interventions to determine which produce the
best learning outcomes is one of the main challenges faced by instructional
designers. Typically, these designers use A/B experiments to evaluate each
intervention; however, it is costly and time consuming to run such studies. To
address this issue, we explore how computational models of learning might
support designers in reasoning causally about alternative interventions within
a fractions tutor. We present an approach for automatically tuning models to
specific individuals and show that personalized models make better predictions
of students' behavior than generic ones. Next, we conduct simulations to
generate counterfactual predictions of performance and learning for two
students (high and low performing) in different versions of the fractions
tutor. Our approach makes predictions that align with previous human findings,
as well as testable predictions that might be evaluated with future human
experiments. | 18 pages, 7 figures | Advances in Cognitive Systems, 10, 35-52 (2023) | cs.AI | [
"cs.AI",
"cs.CY",
"cs.HC"
] |
|
Submodular Maximization Approaches for Equitable Client Selection in
Federated Learning | http://arxiv.org/abs/2408.13683v2 | http://arxiv.org/abs/2408.13683v2 | http://arxiv.org/pdf/2408.13683v2 | 2024-08-24 | 2024-08-27 | [
"Andrés Catalino Castillo Jiménez",
"Ege C. Kaya",
"Lintao Ye",
"Abolfazl Hashemi"
] | [
"",
"",
"",
""
] | In a conventional Federated Learning framework, client selection for training
typically involves the random sampling of a subset of clients in each
iteration. However, this random selection often leads to disparate performance
among clients, raising concerns regarding fairness, particularly in
applications where equitable outcomes are crucial, such as in medical or
financial machine learning tasks. This disparity typically becomes more
pronounced with the advent of performance-centric client sampling techniques.
This paper introduces two novel methods, namely SUBTRUNC and UNIONFL, designed
to address the limitations of random client selection. Both approaches utilize
submodular function maximization to achieve more balanced models. By modifying
the facility location problem, they aim to mitigate the fairness concerns
associated with random selection. SUBTRUNC leverages client loss information to
diversify solutions, while UNIONFL relies on historical client selection data
to ensure a more equitable performance of the final model. Moreover, these
algorithms are accompanied by robust theoretical guarantees regarding
convergence under reasonable assumptions. The efficacy of these methods is
demonstrated through extensive evaluations across heterogeneous scenarios,
revealing significant improvements in fairness as measured by a client
dissimilarity metric. | 13 pages | cs.LG | [
"cs.LG",
"cs.AI",
"cs.SY",
"eess.SP",
"eess.SY"
] |
||
Hierarchical Network Fusion for Multi-Modal Electron Micrograph
Representation Learning with Foundational Large Language Models | http://arxiv.org/abs/2408.13661v1 | http://arxiv.org/abs/2408.13661v1 | http://arxiv.org/pdf/2408.13661v1 | 2024-08-24 | 2024-08-24 | [
"Sakhinana Sagar Srinivas",
"Geethan Sannidhi",
"Venkataramana Runkana"
] | [
"",
"",
""
] | Characterizing materials with electron micrographs is a crucial task in
fields such as semiconductors and quantum materials. The complex hierarchical
structure of micrographs often poses challenges for traditional classification
methods. In this study, we propose an innovative backbone architecture for
analyzing electron micrographs. We create multi-modal representations of the
micrographs by tokenizing them into patch sequences and, additionally,
representing them as vision graphs, commonly referred to as patch attributed
graphs. We introduce the Hierarchical Network Fusion (HNF), a multi-layered
network structure architecture that facilitates information exchange between
the multi-modal representations and knowledge integration across different
patch resolutions. Furthermore, we leverage large language models (LLMs) to
generate detailed technical descriptions of nanomaterials as auxiliary
information to assist in the downstream task. We utilize a cross-modal
attention mechanism for knowledge fusion across cross-domain
representations(both image-based and linguistic insights) to predict the
nanomaterial category. This multi-faceted approach promises a more
comprehensive and accurate representation and classification of micrographs for
nanomaterial identification. Our framework outperforms traditional methods,
overcoming challenges posed by distributional shifts, and facilitating
high-throughput screening. | Our paper is published at the workshop on Robustness of Few-shot and
Zero-shot Learning in Foundation Models at NeurIPS 2023 | cs.CV | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
||
Reactzyme: A Benchmark for Enzyme-Reaction Prediction | http://arxiv.org/abs/2408.13659v1 | http://arxiv.org/abs/2408.13659v1 | http://arxiv.org/pdf/2408.13659v1 | 2024-08-24 | 2024-08-24 | [
"Chenqing Hua",
"Bozitao Zhong",
"Sitao Luan",
"Liang Hong",
"Guy Wolf",
"Doina Precup",
"Shuangjia Zheng"
] | [
"",
"",
"",
"",
"",
"",
""
] | Enzymes, with their specific catalyzed reactions, are necessary for all
aspects of life, enabling diverse biological processes and adaptations.
Predicting enzyme functions is essential for understanding biological pathways,
guiding drug development, enhancing bioproduct yields, and facilitating
evolutionary studies. Addressing the inherent complexities, we introduce a new
approach to annotating enzymes based on their catalyzed reactions. This method
provides detailed insights into specific reactions and is adaptable to newly
discovered reactions, diverging from traditional classifications by protein
family or expert-derived reaction classes. We employ machine learning
algorithms to analyze enzyme reaction datasets, delivering a much more refined
view on the functionality of enzymes. Our evaluation leverages the largest
enzyme-reaction dataset to date, derived from the SwissProt and Rhea databases
with entries up to January 8, 2024. We frame the enzyme-reaction prediction as
a retrieval problem, aiming to rank enzymes by their catalytic ability for
specific reactions. With our model, we can recruit proteins for novel reactions
and predict reactions in novel proteins, facilitating enzyme discovery and
function annotation. | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CE",
"q-bio.QM"
] |
|||
Artificial intelligence for science: The easy and hard problems | http://arxiv.org/abs/2408.14508v1 | http://arxiv.org/abs/2408.14508v1 | http://arxiv.org/pdf/2408.14508v1 | 2024-08-24 | 2024-08-24 | [
"Ruairidh M. Battleday",
"Samuel J. Gershman"
] | [
"",
""
] | A suite of impressive scientific discoveries have been driven by recent
advances in artificial intelligence. These almost all result from training
flexible algorithms to solve difficult optimization problems specified in
advance by teams of domain scientists and engineers with access to large
amounts of data. Although extremely useful, this kind of problem solving only
corresponds to one part of science - the "easy problem." The other part of
scientific research is coming up with the problem itself - the "hard problem."
Solving the hard problem is beyond the capacities of current algorithms for
scientific discovery because it requires continual conceptual revision based on
poorly defined constraints. We can make progress on understanding how humans
solve the hard problem by studying the cognitive science of scientists, and
then use the results to design new computational agents that automatically
infer and update their scientific paradigms. | 16 pages, 3 boxes, 4 figures | cs.AI | [
"cs.AI",
"cs.LG",
"q-bio.NC"
] |
||
Studying the Effect of Audio Filters in Pre-Trained Models for
Environmental Sound Classification | http://arxiv.org/abs/2408.13644v1 | http://arxiv.org/abs/2408.13644v1 | http://arxiv.org/pdf/2408.13644v1 | 2024-08-24 | 2024-08-24 | [
"Aditya Dawn",
"Wazib Ansar"
] | [
"",
""
] | Environmental Sound Classification is an important problem of sound
recognition and is more complicated than speech recognition problems as
environmental sounds are not well structured with respect to time and
frequency. Researchers have used various CNN models to learn audio features
from different audio features like log mel spectrograms, gammatone spectral
coefficients, mel-frequency spectral coefficients, generated from the audio
files, over the past years. In this paper, we propose a new methodology :
Two-Level Classification; the Level 1 Classifier will be responsible to
classify the audio signal into a broader class and the Level 2 Classifiers will
be responsible to find the actual class to which the audio belongs, based on
the output of the Level 1 Classifier. We have also shown the effects of
different audio filters, among which a new method of Audio Crop is introduced
in this paper, which gave the highest accuracies in most of the cases. We have
used the ESC-50 dataset for our experiment and obtained a maximum accuracy of
78.75% in case of Level 1 Classification and 98.04% in case of Level 2
Classifications. | 19 pages, 16 figures | cs.SD | [
"cs.SD",
"cs.AI",
"eess.AS"
] |
||
Temporal Elections: Welfare, Strategyproofness, and Proportionality | http://arxiv.org/abs/2408.13637v1 | http://arxiv.org/abs/2408.13637v1 | http://arxiv.org/pdf/2408.13637v1 | 2024-08-24 | 2024-08-24 | [
"Edith Elkind",
"Tzeh Yuan Neoh",
"Nicholas Teh"
] | [
"",
"",
""
] | We investigate a model of sequential decision-making where a single
alternative is chosen at each round. We focus on two objectives-utilitarian
welfare (Util) and egalitarian welfare (Egal)-and consider the computational
complexity of the associated maximization problems, as well as their
compatibility with strategyproofness and proportionality. We observe that
maximizing Util is easy, but the corresponding decision problem for Egal is
NP-complete even in restricted cases. We complement this hardness result for
Egal with parameterized complexity analysis and an approximation algorithm.
Additionally, we show that, while a mechanism that outputs a Util outcome is
strategyproof, all deterministic mechanisms for computing Egal outcomes fail a
very weak variant of strategyproofness, called non-obvious manipulability
(NOM). However, we show that when agents have non-empty approval sets at each
timestep, choosing an Egal-maximizing outcome while breaking ties
lexicographically satisfies NOM. Regarding proportionality, we prove that a
proportional (PROP) outcome can be computed efficiently, but finding an outcome
that maximizes Util while guaranteeing PROP is NP-hard. We also derive upper
and lower bounds on the price of proportionality with respect to Util and Egal. | Appears in the 27th European Conference on Artificial Intelligence
(ECAI), 2024 | cs.GT | [
"cs.GT",
"cs.AI"
] |
||
DeepVoting: Learning Voting Rules with Tailored Embeddings | http://arxiv.org/abs/2408.13630v1 | http://arxiv.org/abs/2408.13630v1 | http://arxiv.org/pdf/2408.13630v1 | 2024-08-24 | 2024-08-24 | [
"Leonardo Matone",
"Ben Abramowitz",
"Nicholas Mattei",
"Avinash Balakrishnan"
] | [
"",
"",
"",
""
] | Aggregating the preferences of multiple agents into a collective decision is
a common step in many important problems across areas of computer science
including information retrieval, reinforcement learning, and recommender
systems. As Social Choice Theory has shown, the problem of designing algorithms
for aggregation rules with specific properties (axioms) can be difficult, or
provably impossible in some cases. Instead of designing algorithms by hand, one
can learn aggregation rules, particularly voting rules, from data. However, the
prior work in this area has required extremely large models, or been limited by
the choice of preference representation, i.e., embedding. We recast the problem
of designing a good voting rule into one of learning probabilistic versions of
voting rules that output distributions over a set of candidates. Specifically,
we use neural networks to learn probabilistic social choice functions from the
literature. We show that embeddings of preference profiles derived from the
social choice literature allows us to learn existing voting rules more
efficiently and scale to larger populations of voters more easily than other
work if the embedding is tailored to the learning objective. Moreover, we show
that rules learned using embeddings can be tweaked to create novel voting rules
with improved axiomatic properties. Namely, we show that existing voting rules
require only minor modification to combat a probabilistic version of the No
Show Paradox. | cs.MA | [
"cs.MA",
"cs.AI",
"cs.GT",
"cs.LG",
"econ.GN",
"q-fin.EC"
] |
|||
Enhancing Uplift Modeling in Multi-Treatment Marketing Campaigns:
Leveraging Score Ranking and Calibration Techniques | http://arxiv.org/abs/2408.13628v2 | http://arxiv.org/abs/2408.13628v2 | http://arxiv.org/pdf/2408.13628v2 | 2024-08-24 | 2024-08-27 | [
"Yoon Tae Park",
"Ting Xu",
"Mohamed Anany"
] | [
"",
"",
""
] | Uplift modeling is essential for optimizing marketing strategies by selecting
individuals likely to respond positively to specific marketing campaigns. This
importance escalates in multi-treatment marketing campaigns, where diverse
treatment is available and we may want to assign the customers to treatment
that can make the most impact. While there are existing approaches with
convenient frameworks like Causalml, there are potential spaces to enhance the
effect of uplift modeling in multi treatment cases. This paper introduces a
novel approach to uplift modeling in multi-treatment campaigns, leveraging
score ranking and calibration techniques to improve overall performance of the
marketing campaign. We review existing uplift models, including Meta Learner
frameworks (S, T, X), and their application in real-world scenarios.
Additionally, we delve into insights from multi-treatment studies to highlight
the complexities and potential advancements in the field. Our methodology
incorporates Meta-Learner calibration and a scoring rank-based offer selection
strategy. Extensive experiment results with real-world datasets demonstrate the
practical benefits and superior performance of our approach. The findings
underscore the critical role of integrating score ranking and calibration
techniques in refining the performance and reliability of uplift predictions,
thereby advancing predictive modeling in marketing analytics and providing
actionable insights for practitioners seeking to optimize their campaign
strategies. | stat.ML | [
"stat.ML",
"cs.AI",
"cs.LG",
"stat.AP"
] |
|||
Cost-Aware Uncertainty Reduction in Schema Matching with GPT-4: The
Prompt-Matcher Framework | http://arxiv.org/abs/2408.14507v1 | http://arxiv.org/abs/2408.14507v1 | http://arxiv.org/pdf/2408.14507v1 | 2024-08-24 | 2024-08-24 | [
"Longyu Feng",
"Huahang Li",
"Chen Jason Zhang"
] | [
"",
"",
""
] | Schema matching is the process of identifying correspondences between the
elements of two given schemata, essential for database management systems, data
integration, and data warehousing. The inherent uncertainty of current schema
matching algorithms leads to the generation of a set of candidate matches.
Storing these results necessitates the use of databases and systems capable of
handling probabilistic queries. This complicates the querying process and
increases the associated storage costs. Motivated by GPT-4 outstanding
performance, we explore its potential to reduce uncertainty. Our proposal is to
supplant the role of crowdworkers with GPT-4 for querying the set of candidate
matches. To get more precise correspondence verification responses from GPT-4,
We have crafted Semantic-match and Abbreviation-match prompt for GPT-4,
achieving state-of-the-art results on two benchmark datasets DeepMDatasets 100%
(+0.0) and Fabricated-Datasets 91.8% (+2.2) recall rate. To optimise budget
utilisation, we have devised a cost-aware solution. Within the constraints of
the budget, our solution delivers favourable outcomes with minimal time
expenditure.
We introduce a novel framework, Prompt-Matcher, to reduce the uncertainty in
the process of integration of multiple automatic schema matching algorithms and
the selection of complex parameterization. It assists users in diminishing the
uncertainty associated with candidate schema match results and in optimally
ranking the most promising matches. We formally define the Correspondence
Selection Problem, aiming to optimise the revenue within the confines of the
GPT-4 budget. We demonstrate that CSP is NP-Hard and propose an approximation
algorithm with minimal time expenditure. Ultimately, we demonstrate the
efficacy of Prompt-Matcher through rigorous experiments. | cs.DB | [
"cs.DB",
"cs.AI"
] |
|||
Towards Case-based Interpretability for Medical Federated Learning | http://arxiv.org/abs/2408.13626v1 | http://arxiv.org/abs/2408.13626v1 | http://arxiv.org/pdf/2408.13626v1 | 2024-08-24 | 2024-08-24 | [
"Laura Latorre",
"Liliana Petrychenko",
"Regina Beets-Tan",
"Taisiya Kopytova",
"Wilson Silva"
] | [
"",
"",
"",
"",
""
] | We explore deep generative models to generate case-based explanations in a
medical federated learning setting. Explaining AI model decisions through
case-based interpretability is paramount to increasing trust and allowing
widespread adoption of AI in clinical practice. However, medical AI training
paradigms are shifting towards federated learning settings in order to comply
with data protection regulations. In a federated scenario, past data is
inaccessible to the current user. Thus, we use a deep generative model to
generate synthetic examples that protect privacy and explain decisions. Our
proof-of-concept focuses on pleural effusion diagnosis and uses publicly
available Chest X-ray data. | \c{opyright} 20XX IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
future media, including reprinting/republishing this material for advertising
or promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other works | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
No Dataset Needed for Downstream Knowledge Benchmarking: Response
Dispersion Inversely Correlates with Accuracy on Domain-specific QA | http://arxiv.org/abs/2408.13624v1 | http://arxiv.org/abs/2408.13624v1 | http://arxiv.org/pdf/2408.13624v1 | 2024-08-24 | 2024-08-24 | [
"Robert L Simione II"
] | [
""
] | This research seeks to obviate the need for creating QA datasets and grading
(chatbot) LLM responses when comparing LLMs' knowledge in specific topic
domains. This is done in an entirely end-user centric way without need for
access to any inner workings of the LLM, so long as it can be prompted and
given a random seed to create different generations to the same prompt. The
paper does this by, for a given topic domain, defining the "response
dispersion" of an LLM by repeatedly asking an LLM the same opinion question
about that topic domain. Namely, the response dispersion is the count of
singular values needed to explain 95% of the variance in the embedding matrix
of the LLM's responses. It is found that the response dispersion is inversely
correlated with accuracy on relevant QA evaluations (average spearman rank
correlation stronger than -.59). A use-case analysis shows that when comparing
two different LLMs on the same topic domain, comparing their response
dispersion is a suitable replacement for comparing their QA accuracy between
74% and 89% of the time, the range depending on certain reasonable
accuracy-difference tolerances that may be acceptable to an end-user in
exchange for the labor being saved using response dispersion instead of QA
accuracy for comparison. Two response embeddings are studied for creating the
embedding matrix in this study, one is from OpenAI's APIs and one is a novel
embedding, here named reference sentence similarity embeddings, that can be
computed locally and performs very nearly as well in calculating response
dispersion. Also in this research, a pre-existing dataset called the IRC-Wiki
Trivia dataset, originally developed for trivia games, has been re-purposed,
curated, and the curation, called IRC-WikiTriviaQA, is made available for the
purpose of this research. | 16 pages, 3 tables, 1 figure | cs.CL | [
"cs.CL",
"cs.AI",
"I.2.7"
] |
||
Advancing Enterprise Spatio-Temporal Forecasting Applications: Data
Mining Meets Instruction Tuning of Language Models For Multi-modal Time
Series Analysis in Low-Resource Settings | http://arxiv.org/abs/2408.13622v1 | http://arxiv.org/abs/2408.13622v1 | http://arxiv.org/pdf/2408.13622v1 | 2024-08-24 | 2024-08-24 | [
"Sagar Srinivas Sakhinana",
"Geethan Sannidhi",
"Chidaksh Ravuru",
"Venkataramana Runkana"
] | [
"",
"",
"",
""
] | Spatio-temporal forecasting is crucial in transportation, logistics, and
supply chain management. However, current methods struggle with large, complex
datasets. We propose a dynamic, multi-modal approach that integrates the
strengths of traditional forecasting methods and instruction tuning of small
language models for time series trend analysis. This approach utilizes a
mixture of experts (MoE) architecture with parameter-efficient fine-tuning
(PEFT) methods, tailored for consumer hardware to scale up AI solutions in low
resource settings while balancing performance and latency tradeoffs.
Additionally, our approach leverages related past experiences for similar input
time series to efficiently handle both intra-series and inter-series
dependencies of non-stationary data with a time-then-space modeling approach,
using grouped-query attention, while mitigating the limitations of traditional
forecasting techniques in handling distributional shifts. Our approach models
predictive uncertainty to improve decision-making. Our framework enables
on-premises customization with reduced computational and memory demands, while
maintaining inference speed and data privacy/security. Extensive experiments on
various real-world datasets demonstrate that our framework provides robust and
accurate forecasts, significantly outperforming existing methods. | Published at the ICLR 2024 Workshop on Practical ML for Low Resource
Settings(PML4LRS) | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
Preliminary Investigations of a Multi-Faceted Robust and Synergistic
Approach in Semiconductor Electron Micrograph Analysis: Integrating Vision
Transformers with Large Language and Multimodal Models | http://arxiv.org/abs/2408.13621v1 | http://arxiv.org/abs/2408.13621v1 | http://arxiv.org/pdf/2408.13621v1 | 2024-08-24 | 2024-08-24 | [
"Sakhinana Sagar Srinivas",
"Geethan Sannidhi",
"Sreeja Gangasani",
"Chidaksh Ravuru",
"Venkataramana Runkana"
] | [
"",
"",
"",
"",
""
] | Characterizing materials using electron micrographs is crucial in areas such
as semiconductors and quantum materials. Traditional classification methods
falter due to the intricatestructures of these micrographs. This study
introduces an innovative architecture that leverages the generative
capabilities of zero-shot prompting in Large Language Models (LLMs) such as
GPT-4(language only), the predictive ability of few-shot (in-context) learning
in Large Multimodal Models (LMMs) such as GPT-4(V)ision, and fuses knowledge
across image based and linguistic insights for accurate nanomaterial category
prediction. This comprehensive approach aims to provide a robust solution for
the automated nanomaterial identification task in semiconductor manufacturing,
blending performance, efficiency, and interpretability. Our method surpasses
conventional approaches, offering precise nanomaterial identification and
facilitating high-throughput screening. | Published at Deployable AI (DAI) Workshop at AAAI-2024 | cs.CV | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] |
||
Balancing Diversity and Risk in LLM Sampling: How to Select Your Method
and Parameter for Open-Ended Text Generation | http://arxiv.org/abs/2408.13586v1 | http://arxiv.org/abs/2408.13586v1 | http://arxiv.org/pdf/2408.13586v1 | 2024-08-24 | 2024-08-24 | [
"Yuxuan Zhou",
"Margret Keuper",
"Mario Fritz"
] | [
"",
"",
""
] | Sampling-based decoding strategies have been widely adopted for Large
Language Models (LLMs) in numerous applications, which target a balance between
diversity and quality via temperature tuning and tail truncation (e.g., top-k
and top-p sampling). Considering the high dynamic range of the candidate
next-token given different prefixes, recent studies propose to adaptively
truncate the tail of LLM's predicted distribution. Although improved results
haven been reported with these methods on open-ended text generation tasks, the
results are highly dependent on the curated truncation parameters and exemplar
text. In this paper, we propose a systematic way to estimate the intrinsic
capacity of a truncation sampling method by considering the trade-off between
diversity and risk at each decoding step, based on our collected prefix tree
which preserves the context of a full sentence. Our work provides a
comprehensive comparison between existing truncation sampling methods, as well
as their recommended parameters as a guideline for users. | cs.CL | [
"cs.CL",
"cs.AI"
] |
|||
Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular
Networks with Double Dynamics | http://arxiv.org/abs/2408.13546v1 | http://arxiv.org/abs/2408.13546v1 | http://arxiv.org/pdf/2408.13546v1 | 2024-08-24 | 2024-08-24 | [
"Zonghui Yang",
"Shijian Gao",
"Xiang Cheng",
"Liuqing Yang"
] | [
"",
"",
"",
""
] | Integrated sensing and communication (ISAC) technology plays a crucial role
in vehicular networks. However, the communication channel within this context
exhibits time-varying characteristics, and potential targets may move rapidly,
resulting in double dynamics. These presents significant challenges for
real-time ISAC precoding design that have not been thoroughly explored. While
optimization-based precoding methods have been extensively studied, they are
computationally complex and heavily rely on perfect prior information that is
rarely available in situations with double dynamics. In this paper, we propose
a synesthesia of machine (SoM)-enhanced precoding paradigm, where the base
station leverages various modalities such as positioning and channel
information to adapt to double dynamics, and effectively utilizes environmental
information to stretch ISAC performance boundaries through a deep reinforcement
learning framework. Additionally, a parameter-shared actor-critic architecture
is tailored to expedite training in complex state and action spaces. Extensive
experimental validation has demonstrated the multifaceted superiority of our
method over existing approaches. | 13 pages, 17 figures, 4 tables | eess.SP | [
"eess.SP",
"cs.AI"
] |
||
Selective Preference Optimization via Token-Level Reward Function
Estimation | http://arxiv.org/abs/2408.13518v1 | http://arxiv.org/abs/2408.13518v1 | http://arxiv.org/pdf/2408.13518v1 | 2024-08-24 | 2024-08-24 | [
"Kailai Yang",
"Zhiwei Liu",
"Qianqian Xie",
"Jimin Huang",
"Erxue Min",
"Sophia Ananiadou"
] | [
"",
"",
"",
"",
"",
""
] | Recent advancements in large language model alignment leverage token-level
supervisions to perform fine-grained preference optimization. However, existing
token-level alignment methods either optimize on all available tokens, which
can be noisy and inefficient, or perform selective training with complex and
expensive key token selection strategies. In this work, we propose Selective
Preference Optimization (SePO), a novel selective alignment strategy that
centers on efficient key token selection. SePO proposes the first token
selection method based on Direct Preference Optimization (DPO), which trains an
oracle model to estimate a token-level reward function on the target data. This
method applies to any existing alignment datasets with response-level
annotations and enables cost-efficient token selection with small-scale oracle
models and training data. The estimated reward function is then utilized to
score all tokens within the target dataset, where only the key tokens are
selected to supervise the target policy model with a reference model-free
contrastive objective function. Extensive experiments on three public
evaluation benchmarks show that SePO significantly outperforms competitive
baseline methods by only optimizing 30% key tokens on the target dataset. SePO
applications on weak-to-strong generalization show that weak oracle models
effectively supervise strong policy models with up to 16.8x more parameters.
SePO also effectively selects key tokens from out-of-distribution data to
enhance strong policy models and alleviate the over-optimization problem. | Work in progress | cs.CL | [
"cs.CL",
"cs.AI",
"cs.LG"
] |
||
AnoPLe: Few-Shot Anomaly Detection via Bi-directional Prompt Learning
with Only Normal Samples | http://arxiv.org/abs/2408.13516v1 | http://arxiv.org/abs/2408.13516v1 | http://arxiv.org/pdf/2408.13516v1 | 2024-08-24 | 2024-08-24 | [
"Yujin Lee",
"Seoyoon Jang",
"Hyunsoo Yoon"
] | [
"",
"",
""
] | Few-shot Anomaly Detection (FAD) poses significant challenges due to the
limited availability of training samples and the frequent absence of abnormal
samples. Previous approaches often rely on annotations or true abnormal samples
to improve detection, but such textual or visual cues are not always
accessible. To address this, we introduce AnoPLe, a multi-modal prompt learning
method designed for anomaly detection without prior knowledge of anomalies.
AnoPLe simulates anomalies and employs bidirectional coupling of textual and
visual prompts to facilitate deep interaction between the two modalities.
Additionally, we integrate a lightweight decoder with a learnable multi-view
signal, trained on multi-scale images to enhance local semantic comprehension.
To further improve performance, we align global and local semantics, enriching
the image-level understanding of anomalies. The experimental results
demonstrate that AnoPLe achieves strong FAD performance, recording 94.1% and
86.2% Image AUROC on MVTec-AD and VisA respectively, with only around a 1% gap
compared to the SoTA, despite not being exposed to true anomalies. Code is
available at https://github.com/YoojLee/AnoPLe. | Code is available at https://github.com/YoojLee/AnoPLe | cs.CV | [
"cs.CV",
"cs.AI"
] |
||
Empowering Pre-Trained Language Models for Spatio-Temporal Forecasting
via Decoupling Enhanced Discrete Reprogramming | http://arxiv.org/abs/2408.14505v1 | http://arxiv.org/abs/2408.14505v1 | http://arxiv.org/pdf/2408.14505v1 | 2024-08-24 | 2024-08-24 | [
"Hao Wang",
"Jindong Han",
"Wei Fan",
"Hao Liu"
] | [
"",
"",
"",
""
] | Spatio-temporal time series forecasting plays a critical role in various
real-world applications, such as transportation optimization, energy
management, and climate analysis. The recent advancements in Pre-trained
Language Models (PLMs) have inspired efforts to reprogram these models for time
series forecasting tasks, by leveraging their superior reasoning and
generalization capabilities. However, existing approaches fall short in
handling complex spatial inter-series dependencies and intrinsic intra-series
frequency components, limiting their spatio-temporal forecasting performance.
Moreover, the linear mapping of continuous time series to a compressed subset
vocabulary in reprogramming constrains the spatio-temporal semantic
expressivity of PLMs and may lead to potential information bottleneck. To
overcome the above limitations, we propose \textsc{RePST}, a tailored PLM
reprogramming framework for spatio-temporal forecasting. The key insight of
\textsc{RePST} is to decouple the spatio-temporal dynamics in the frequency
domain, allowing better alignment with the PLM text space. Specifically, we
first decouple spatio-temporal data in Fourier space and devise a structural
diffusion operator to obtain temporal intrinsic and spatial diffusion signals,
making the dynamics more comprehensible and predictable for PLMs. To avoid
information bottleneck from a limited vocabulary, we further propose a discrete
reprogramming strategy that selects relevant discrete textual information from
an expanded vocabulary space in a differentiable manner. Extensive experiments
on four real-world datasets show that our proposed approach significantly
outperforms state-of-the-art spatio-temporal forecasting models, particularly
in data-scarce scenarios. | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CL"
] |
|||
Is Functional Correctness Enough to Evaluate Code Language Models?
Exploring Diversity of Generated Codes | http://arxiv.org/abs/2408.14504v1 | http://arxiv.org/abs/2408.14504v1 | http://arxiv.org/pdf/2408.14504v1 | 2024-08-24 | 2024-08-24 | [
"Heejae Chon",
"Seonghyeon Lee",
"Jinyoung Yeo",
"Dongha Lee"
] | [
"",
"",
"",
""
] | Language models (LMs) have exhibited impressive abilities in generating codes
from natural language requirements. In this work, we highlight the diversity of
code generated by LMs as a critical criterion for evaluating their code
generation capabilities, in addition to functional correctness. Despite its
practical implications, there is a lack of studies focused on assessing the
diversity of generated code, which overlooks its importance in the development
of code LMs. We propose a systematic approach to evaluate the diversity of
generated code, utilizing various metrics for inter-code similarity as well as
functional correctness. Specifically, we introduce a pairwise code similarity
measure that leverages large LMs' capabilities in code understanding and
reasoning, demonstrating the highest correlation with human judgment. We
extensively investigate the impact of various factors on the quality of
generated code, including model sizes, temperatures, training approaches,
prompting strategies, and the difficulty of input problems. Our consistent
observation of a positive correlation between the test pass score and the
inter-code similarity score indicates that current LMs tend to produce
functionally correct code with limited diversity. | 15pages, 6 figures, 8 tables | cs.SE | [
"cs.SE",
"cs.AI",
"cs.PL"
] |
||
Thresholded Lexicographic Ordered Multiobjective Reinforcement Learning | http://arxiv.org/abs/2408.13493v1 | http://arxiv.org/abs/2408.13493v1 | http://arxiv.org/pdf/2408.13493v1 | 2024-08-24 | 2024-08-24 | [
"Alperen Tercan",
"Vinayak S. Prabhu"
] | [
"",
""
] | Lexicographic multi-objective problems, which impose a lexicographic
importance order over the objectives, arise in many real-life scenarios.
Existing Reinforcement Learning work directly addressing lexicographic tasks
has been scarce. The few proposed approaches were all noted to be heuristics
without theoretical guarantees as the Bellman equation is not applicable to
them. Additionally, the practical applicability of these prior approaches also
suffers from various issues such as not being able to reach the goal state.
While some of these issues have been known before, in this work we investigate
further shortcomings, and propose fixes for improving practical performance in
many cases. We also present a policy optimization approach using our
Lexicographic Projection Optimization (LPO) algorithm that has the potential to
address these theoretical and practical concerns. Finally, we demonstrate our
proposed algorithms on benchmark problems. | Full version of ECAI 2024 paper | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
MPruner: Optimizing Neural Network Size with CKA-Based Mutual
Information Pruning | http://arxiv.org/abs/2408.13482v1 | http://arxiv.org/abs/2408.13482v1 | http://arxiv.org/pdf/2408.13482v1 | 2024-08-24 | 2024-08-24 | [
"Seungbeom Hu",
"ChanJun Park",
"Andrew Ferraiuolo",
"Sang-Ki Ko",
"Jinwoo Kim",
"Haein Song",
"Jieung Kim"
] | [
"",
"",
"",
"",
"",
"",
""
] | Determining the optimal size of a neural network is critical, as it directly
impacts runtime performance and memory usage. Pruning is a well-established
model compression technique that reduces the size of neural networks while
mathematically guaranteeing accuracy preservation. However, many recent pruning
methods overlook the global contributions of individual model components,
making it difficult to ensure that a pruned model meets the desired dataset and
performance requirements. To address these challenges, we developed a new
pruning algorithm, MPruner, that leverages mutual information through vector
similarity. MPruner utilizes layer clustering with the Centered Kernel
Alignment (CKA) similarity metric, allowing us to incorporate global
information from the neural network for more precise and efficient layer-wise
pruning. We evaluated MPruner across various architectures and configurations,
demonstrating its versatility and providing practical guidelines. MPruner
achieved up to a 50% reduction in parameters and memory usage for CNN and
transformer-based models, with minimal to no loss in accuracy. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
Disentangled Generative Graph Representation Learning | http://arxiv.org/abs/2408.13471v1 | http://arxiv.org/abs/2408.13471v1 | http://arxiv.org/pdf/2408.13471v1 | 2024-08-24 | 2024-08-24 | [
"Xinyue Hu",
"Zhibin Duan",
"Xinyang Liu",
"Yuxin Li",
"Bo Chen",
"Mingyuan Zhou"
] | [
"",
"",
"",
"",
"",
""
] | Recently, generative graph models have shown promising results in learning
graph representations through self-supervised methods. However, most existing
generative graph representation learning (GRL) approaches rely on random
masking across the entire graph, which overlooks the entanglement of learned
representations. This oversight results in non-robustness and a lack of
explainability. Furthermore, disentangling the learned representations remains
a significant challenge and has not been sufficiently explored in GRL research.
Based on these insights, this paper introduces DiGGR (Disentangled Generative
Graph Representation Learning), a self-supervised learning framework. DiGGR
aims to learn latent disentangled factors and utilizes them to guide graph mask
modeling, thereby enhancing the disentanglement of learned representations and
enabling end-to-end joint learning. Extensive experiments on 11 public datasets
for two different graph learning tasks demonstrate that DiGGR consistently
outperforms many previous self-supervised methods, verifying the effectiveness
of the proposed approach. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
LlamaDuo: LLMOps Pipeline for Seamless Migration from Service LLMs to
Small-Scale Local LLMs | http://arxiv.org/abs/2408.13467v2 | http://arxiv.org/abs/2408.13467v2 | http://arxiv.org/pdf/2408.13467v2 | 2024-08-24 | 2024-08-29 | [
"Chansung Park",
"Juyong Jiang",
"Fan Wang",
"Sayak Paul",
"Jing Tang"
] | [
"",
"",
"",
"",
""
] | The widespread adoption of cloud-based proprietary large language models
(LLMs) has introduced significant challenges, including operational
dependencies, privacy concerns, and the necessity of continuous internet
connectivity. In this work, we introduce an LLMOps pipeline, "LlamaDuo", for
the seamless migration of knowledge and abilities from service-oriented LLMs to
smaller, locally manageable models. This pipeline is crucial for ensuring
service continuity in the presence of operational failures, strict privacy
policies, or offline requirements. Our LlamaDuo involves fine-tuning a small
language model against the service LLM using a synthetic dataset generated by
the latter. If the performance of the fine-tuned model falls short of
expectations, it is enhanced by further fine-tuning with additional similar
data created by the service LLM. This iterative process guarantees that the
smaller model can eventually match or even surpass the service LLM's
capabilities in specific downstream tasks, offering a practical and scalable
solution for managing AI deployments in constrained environments. Extensive
experiments with leading edge LLMs are conducted to demonstrate the
effectiveness, adaptability, and affordability of LlamaDuo across various
downstream tasks. Our pipeline implementation is available at
https://github.com/deep-diver/llamaduo. | 28 pages, 18 figures, 6 tables | cs.LG | [
"cs.LG",
"cs.AI",
"cs.DC"
] |
||
Uncovering Biases with Reflective Large Language Models | http://arxiv.org/abs/2408.13464v1 | http://arxiv.org/abs/2408.13464v1 | http://arxiv.org/pdf/2408.13464v1 | 2024-08-24 | 2024-08-24 | [
"Edward Y. Chang"
] | [
""
] | Biases inherent in human endeavors pose significant challenges for machine
learning, particularly in supervised learning that relies on potentially biased
"ground truth" data. This reliance, coupled with models' tendency to generalize
based on statistical maximal likelihood, can propagate and amplify biases,
exacerbating societal issues. To address this, our study proposes a reflective
methodology utilizing multiple Large Language Models (LLMs) engaged in a
dynamic dialogue to uncover diverse perspectives. By leveraging conditional
statistics, information theory, and divergence metrics, this novel approach
fosters context-dependent linguistic behaviors, promoting unbiased outputs.
Furthermore, it enables measurable progress tracking and explainable
remediation actions to address identified biases. | 16 pages, 3 figures, 8 tables | cs.AI | [
"cs.AI",
"cs.CL",
"cs.LG",
"I.2.7"
] |
||
Probing the Robustness of Vision-Language Pretrained Models: A
Multimodal Adversarial Attack Approach | http://arxiv.org/abs/2408.13461v1 | http://arxiv.org/abs/2408.13461v1 | http://arxiv.org/pdf/2408.13461v1 | 2024-08-24 | 2024-08-24 | [
"Jiwei Guan",
"Tianyu Ding",
"Longbing Cao",
"Lei Pan",
"Chen Wang",
"Xi Zheng"
] | [
"",
"",
"",
"",
"",
""
] | Vision-language pretraining (VLP) with transformers has demonstrated
exceptional performance across numerous multimodal tasks. However, the
adversarial robustness of these models has not been thoroughly investigated.
Existing multimodal attack methods have largely overlooked cross-modal
interactions between visual and textual modalities, particularly in the context
of cross-attention mechanisms. In this paper, we study the adversarial
vulnerability of recent VLP transformers and design a novel Joint Multimodal
Transformer Feature Attack (JMTFA) that concurrently introduces adversarial
perturbations in both visual and textual modalities under white-box settings.
JMTFA strategically targets attention relevance scores to disrupt important
features within each modality, generating adversarial samples by fusing
perturbations and leading to erroneous model predictions. Experimental results
indicate that the proposed approach achieves high attack success rates on
vision-language understanding and reasoning downstream tasks compared to
existing baselines. Notably, our findings reveal that the textual modality
significantly influences the complex fusion processes within VLP transformers.
Moreover, we observe no apparent relationship between model size and
adversarial robustness under our proposed attacks. These insights emphasize a
new dimension of adversarial robustness and underscore potential risks in the
reliable deployment of multimodal AI systems. | cs.CV | [
"cs.CV",
"cs.AI"
] |
|||
Make Every Penny Count: Difficulty-Adaptive Self-Consistency for
Cost-Efficient Reasoning | http://arxiv.org/abs/2408.13457v1 | http://arxiv.org/abs/2408.13457v1 | http://arxiv.org/pdf/2408.13457v1 | 2024-08-24 | 2024-08-24 | [
"Xinglin Wang",
"Shaoxiong Feng",
"Yiwei Li",
"Peiwen Yuan",
"Yueqi Zhang",
"Boyuan Pan",
"Heda Wang",
"Yao Hu",
"Kan Li"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
""
] | Self-consistency (SC), a widely used decoding strategy for chain-of-thought
reasoning, shows significant gains across various multi-step reasoning tasks
but comes with a high cost due to multiple sampling with the preset size. Its
variants, Adaptive self-consistency (ASC) and Early-stopping self-consistency
(ESC), dynamically adjust the number of samples based on the posterior
distribution of a set of pre-samples, reducing the cost of SC with minimal
impact on performance. Both methods, however, do not exploit the prior
information about question difficulty. It often results in unnecessary repeated
sampling for easy questions that could be accurately answered with just one
attempt, wasting resources. To tackle this problem, we propose
Difficulty-Adaptive Self-Consistency (DSC), which leverages the difficulty
information from both prior and posterior perspectives to adaptively allocate
inference resources, further reducing the cost of SC. To demonstrate the
effectiveness of DSC, we conduct extensive experiments on three popular
categories of reasoning tasks: arithmetic, commonsense and symbolic reasoning
on six benchmarks. The empirical results show that DSC consistently surpasses
the strong baseline ASC and ESC in terms of costs by a significant margin,
while attaining comparable performances. | Preprint | cs.CL | [
"cs.CL",
"cs.AI"
] |
||
A Law of Next-Token Prediction in Large Language Models | http://arxiv.org/abs/2408.13442v1 | http://arxiv.org/abs/2408.13442v1 | http://arxiv.org/pdf/2408.13442v1 | 2024-08-24 | 2024-08-24 | [
"Hangfeng He",
"Weijie J. Su"
] | [
"",
""
] | Large language models (LLMs) have been widely employed across various
application domains, yet their black-box nature poses significant challenges to
understanding how these models process input data internally to make
predictions. In this paper, we introduce a precise and quantitative law that
governs the learning of contextualized token embeddings through intermediate
layers in pre-trained LLMs for next-token prediction. Our findings reveal that
each layer contributes equally to enhancing prediction accuracy, from the
lowest to the highest layer -- a universal phenomenon observed across a diverse
array of open-source LLMs, built on architectures such as Transformer, RWKV,
and Mamba. We demonstrate that this law offers new perspectives and insights to
inform and guide practices in LLM development and applications, including model
scaling, pre-training tasks, and information flow. Overall, our law enables
more fine-grained approaches to the design, training, and interpretation of
LLMs through scrutinizing their internal data processing mechanisms. | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CL",
"stat.ML"
] |
|||
Applying graph neural network to SupplyGraph for supply chain network | http://arxiv.org/abs/2408.14501v1 | http://arxiv.org/abs/2408.14501v1 | http://arxiv.org/pdf/2408.14501v1 | 2024-08-23 | 2024-08-23 | [
"Kihwan Han"
] | [
""
] | Supply chain networks describe interactions between products, manufacture
facilities, storages in the context of supply and demand of the products.
Supply chain data are inherently under graph structure; thus, it can be fertile
ground for applications of graph neural network (GNN). Very recently, supply
chain dataset, SupplyGraph, has been released to the public. Though the
SupplyGraph dataset is valuable given scarcity of publicly available data,
there was less clarity on description of the dataset, data quality assurance
process, and hyperparameters of the selected models. Further, for
generalizability of findings, it would be more convincing to present the
findings by performing statistical analyses on the distribution of errors
rather than showing the average value of the errors. Therefore, this study
assessed the supply chain dataset, SupplyGraph, with better clarity on analyses
processes, data quality assurance, machine learning (ML) model specifications.
After data quality assurance procedures, this study compared performance of
Multilayer Perceptions (MLP), Graph Convolution Network (GCN), and Graph
Attention Network (GAT) on a demanding forecasting task while matching
hyperparameters as feasible as possible. The analyses revealed that GAT
performed best, followed by GCN and MLP. Those performance improvements were
statistically significant at $\alpha = 0.05$ after correction for multiple
comparisons. This study also discussed several considerations in applying GNN
to supply chain networks. The current study reinforces the previous study in
supply chain benchmark dataset with respect to description of the dataset and
methodology, so that the future research in applications of GNN to supply chain
becomes more reproducible. | 8 pages, 5 figures | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
Optimizing Collaboration of LLM based Agents for Finite Element Analysis | http://arxiv.org/abs/2408.13406v1 | http://arxiv.org/abs/2408.13406v1 | http://arxiv.org/pdf/2408.13406v1 | 2024-08-23 | 2024-08-23 | [
"Chuan Tian",
"Yilei Zhang"
] | [
"",
""
] | This paper investigates the interactions between multiple agents within Large
Language Models (LLMs) in the context of programming and coding tasks. We
utilize the AutoGen framework to facilitate communication among agents,
evaluating different configurations based on the success rates from 40 random
runs for each setup. The study focuses on developing a flexible automation
framework for applying the Finite Element Method (FEM) to solve linear elastic
problems. Our findings emphasize the importance of optimizing agent roles and
clearly defining their responsibilities, rather than merely increasing the
number of agents. Effective collaboration among agents is shown to be crucial
for addressing general FEM challenges. This research demonstrates the potential
of LLM multi-agent systems to enhance computational automation in simulation
methodologies, paving the way for future advancements in engineering and
artificial intelligence. | cs.AI | [
"cs.AI",
"cs.CE",
"cs.MA"
] |
|||
Transforming Location Retrieval at Airbnb: A Journey from Heuristics to
Reinforcement Learning | http://arxiv.org/abs/2408.13399v1 | http://arxiv.org/abs/2408.13399v1 | http://arxiv.org/pdf/2408.13399v1 | 2024-08-23 | 2024-08-23 | [
"Dillon Davis",
"Huiji Gao",
"Weiwei Guo",
"Thomas Legrand",
"Malay Haldar",
"Alex Deng",
"Han Zhao",
"Liwei He",
"Sanjeev Katariya"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
""
] | The Airbnb search system grapples with many unique challenges as it continues
to evolve. We oversee a marketplace that is nuanced by geography, diversity of
homes, and guests with a variety of preferences. Crafting an efficient search
system that can accommodate diverse guest needs, while showcasing relevant
homes lies at the heart of Airbnb's success. Airbnb search has many challenges
that parallel other recommendation and search systems but it has a unique
information retrieval problem, upstream of ranking, called location retrieval.
It requires defining a topological map area that is relevant to the searched
query for homes listing retrieval. The purpose of this paper is to demonstrate
the methodology, challenges, and impact of building a machine learning based
location retrieval product from the ground up. Despite the lack of suitable,
prevalent machine learning based approaches, we tackle cold start,
generalization, differentiation and algorithmic bias. We detail the efficacy of
heuristics, statistics, machine learning, and reinforcement learning approaches
to solve these challenges, particularly for systems that are often unexplored
by current literature. | cs.IR | [
"cs.IR",
"cs.AI"
] |
|||
N-DriverMotion: Driver motion learning and prediction using an
event-based camera and directly trained spiking neural networks | http://arxiv.org/abs/2408.13379v1 | http://arxiv.org/abs/2408.13379v1 | http://arxiv.org/pdf/2408.13379v1 | 2024-08-23 | 2024-08-23 | [
"Hyo Jong Chung",
"Byungkon Kang",
"Yoonseok Yang"
] | [
"",
"",
""
] | Driver motion recognition is a principal factor in ensuring the safety of
driving systems. This paper presents a novel system for learning and predicting
driver motions and an event-based high-resolution (1280x720) dataset,
N-DriverMotion, newly collected to train on a neuromorphic vision system. The
system comprises an event-based camera that generates the first high-resolution
driver motion dataset representing spike inputs and efficient spiking neural
networks (SNNs) that are effective in training and predicting the driver's
gestures. The event dataset consists of 13 driver motion categories classified
by direction (front, side), illumination (bright, moderate, dark), and
participant. A novel simplified four-layer convolutional spiking neural network
(CSNN) that we proposed was directly trained using the high-resolution dataset
without any time-consuming preprocessing. This enables efficient adaptation to
on-device SNNs for real-time inference on high-resolution event-based streams.
Compared with recent gesture recognition systems adopting neural networks for
vision processing, the proposed neuromorphic vision system achieves comparable
accuracy, 94.04\%, in recognizing driver motions with the CSNN architecture.
Our proposed CSNN and the dataset can be used to develop safer and more
efficient driver monitoring systems for autonomous vehicles or edge devices
requiring an efficient neural network architecture. | 10 pages, 5 figures | cs.CV | [
"cs.CV",
"cs.AI",
"68T45",
"I.4.8; I.4.9"
] |
||
DrugAgent: Explainable Drug Repurposing Agent with Large Language
Model-based Reasoning | http://arxiv.org/abs/2408.13378v1 | http://arxiv.org/abs/2408.13378v1 | http://arxiv.org/pdf/2408.13378v1 | 2024-08-23 | 2024-08-23 | [
"Yoshitaka Inoue",
"Tianci Song",
"Tianfan Fu"
] | [
"",
"",
""
] | Drug repurposing offers a promising avenue for accelerating drug development
by identifying new therapeutic potentials of existing drugs. In this paper, we
propose a multi-agent framework to enhance the drug repurposing process using
state-of-the-art machine learning techniques and knowledge integration. Our
framework comprises several specialized agents: an AI Agent trains robust
drug-target interaction (DTI) models; a Knowledge Graph Agent utilizes the
drug-gene interaction database (DGIdb), DrugBank, Comparative Toxicogenomics
Database (CTD), and Search Tool for Interactions of Chemicals (STITCH) to
systematically extract DTIs; and a Search Agent interacts with biomedical
literature to annotate and verify computational predictions. By integrating
outputs from these agents, our system effectively harnesses diverse data
sources, including external databases, to propose viable repurposing
candidates. Preliminary results demonstrate the potential of our approach in
not only predicting drug-disease interactions but also in reducing the time and
cost associated with traditional drug discovery methods. This paper highlights
the scalability of multi-agent systems in biomedical research and their role in
driving innovation in drug repurposing. Our approach not only outperforms
existing methods in predicting drug repurposing potential but also provides
interpretable results, paving the way for more efficient and cost-effective
drug discovery processes. | 18 pages, 1 figure | cs.AI | [
"cs.AI",
"cs.CL",
"cs.IR",
"cs.LG",
"q-bio.QM"
] |
||
Reduce, Reuse, Recycle: Categories for Compositional Reinforcement
Learning | http://arxiv.org/abs/2408.13376v1 | http://arxiv.org/abs/2408.13376v1 | http://arxiv.org/pdf/2408.13376v1 | 2024-08-23 | 2024-08-23 | [
"Georgios Bakirtzis",
"Michail Savvas",
"Ruihan Zhao",
"Sandeep Chinchali",
"Ufuk Topcu"
] | [
"",
"",
"",
"",
""
] | In reinforcement learning, conducting task composition by forming cohesive,
executable sequences from multiple tasks remains challenging. However, the
ability to (de)compose tasks is a linchpin in developing robotic systems
capable of learning complex behaviors. Yet, compositional reinforcement
learning is beset with difficulties, including the high dimensionality of the
problem space, scarcity of rewards, and absence of system robustness after task
composition. To surmount these challenges, we view task composition through the
prism of category theory -- a mathematical discipline exploring structures and
their compositional relationships. The categorical properties of Markov
decision processes untangle complex tasks into manageable sub-tasks, allowing
for strategical reduction of dimensionality, facilitating more tractable reward
structures, and bolstering system robustness. Experimental results support the
categorical theory of reinforcement learning by enabling skill reduction,
reuse, and recycling when learning complex robotic arm tasks. | ECAI 2024 | cs.AI | [
"cs.AI",
"cs.LG",
"cs.SY",
"eess.SY",
"math.CT"
] |
||
Understanding Defects in Generated Codes by Language Models | http://arxiv.org/abs/2408.13372v1 | http://arxiv.org/abs/2408.13372v1 | http://arxiv.org/pdf/2408.13372v1 | 2024-08-23 | 2024-08-23 | [
"Ali Mohammadi Esfahani",
"Nafiseh Kahani",
"Samuel A. Ajila"
] | [
"",
"",
""
] | This study investigates the reliability of code generation by Large Language
Models (LLMs), focusing on identifying and analyzing defects in the generated
code. Despite the advanced capabilities of LLMs in automating code generation,
ensuring the accuracy and functionality of the output remains a significant
challenge. By using a structured defect classification method to understand
their nature and origins this study categorizes and analyzes 367 identified
defects from code snippets generated by LLMs, with a significant proportion
being functionality and algorithm errors. These error categories indicate key
areas where LLMs frequently fail, underscoring the need for targeted
improvements. To enhance the accuracy of code generation, this paper
implemented five prompt engineering techniques, including Scratchpad Prompting,
Program of Thoughts Prompting, Chain-of-Thought Prompting, Chain of Code
Prompting, and Structured Chain-of-Thought Prompting. These techniques were
applied to refine the input prompts, aiming to reduce ambiguities and improve
the models' accuracy rate. The research findings suggest that precise and
structured prompting significantly mitigates common defects, thereby increasing
the reliability of LLM-generated code. | cs.SE | [
"cs.SE",
"cs.AI"
] |
|||
CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations
of Research Papers | http://arxiv.org/abs/2408.13366v1 | http://arxiv.org/abs/2408.13366v1 | http://arxiv.org/pdf/2408.13366v1 | 2024-08-23 | 2024-08-23 | [
"Ekaterina Trofimova",
"Emil Sataev",
"Abhijit Singh Jowhari"
] | [
"",
"",
""
] | This paper presents CodeRefine, a novel framework for automatically
transforming research paper methodologies into functional code using Large
Language Models (LLMs). Our multi-step approach first extracts and summarizes
key text chunks from papers, analyzes their code relevance, and creates a
knowledge graph using a predefined ontology. Code is then generated from this
structured representation and enhanced through a proposed retrospective
retrieval-augmented generation approach. CodeRefine addresses the challenge of
bridging theoretical research and practical implementation, offering a more
accurate alternative to LLM zero-shot prompting. Evaluations on diverse
scientific papers demonstrate CodeRefine's ability to improve code
implementation from the paper, potentially accelerating the adoption of
cutting-edge algorithms in real-world applications. | cs.CL | [
"cs.CL",
"cs.AI",
"cs.LG"
] |
|||
Reconciling Different Theories of Learning with an Agent-based Model of
Procedural Learning | http://arxiv.org/abs/2408.13364v1 | http://arxiv.org/abs/2408.13364v1 | http://arxiv.org/pdf/2408.13364v1 | 2024-08-23 | 2024-08-23 | [
"Sina Rismanchian",
"Shayan Doroudi"
] | [
"",
""
] | Computational models of human learning can play a significant role in
enhancing our knowledge about nuances in theoretical and qualitative learning
theories and frameworks. There are many existing frameworks in educational
settings that have shown to be verified using empirical studies, but at times
we find these theories make conflicting claims or recommendations for
instruction. In this study, we propose a new computational model of human
learning, Procedural ABICAP, that reconciles the ICAP,
Knowledge-Learning-Instruction (KLI), and cognitive load theory (CLT)
frameworks for learning procedural knowledge. ICAP assumes that constructive
learning generally yields better learning outcomes, while theories such as KLI
and CLT claim that this is not always true. We suppose that one reason for this
may be that ICAP is primarily used for conceptual learning and is
underspecified as a framework for thinking about procedural learning. We show
how our computational model, both by design and through simulations, can be
used to reconcile different results in the literature. More generally, we
position our computational model as an executable theory of learning that can
be used to simulate various educational settings. | cs.CY | [
"cs.CY",
"cs.AI"
] |
|||
Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate
Scheduler | http://arxiv.org/abs/2408.13359v1 | http://arxiv.org/abs/2408.13359v1 | http://arxiv.org/pdf/2408.13359v1 | 2024-08-23 | 2024-08-23 | [
"Yikang Shen",
"Matthew Stallone",
"Mayank Mishra",
"Gaoyuan Zhang",
"Shawn Tan",
"Aditya Prasad",
"Adriana Meza Soria",
"David D. Cox",
"Rameswar Panda"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
""
] | Finding the optimal learning rate for language model pretraining is a
challenging task. This is not only because there is a complicated correlation
between learning rate, batch size, number of training tokens, model size, and
other hyperparameters but also because it is prohibitively expensive to perform
a hyperparameter search for large language models with Billions or Trillions of
parameters. Recent studies propose using small proxy models and small corpus to
perform hyperparameter searches and transposing the optimal parameters to large
models and large corpus. While the zero-shot transferability is theoretically
and empirically proven for model size related hyperparameters, like depth and
width, the zero-shot transfer from small corpus to large corpus is
underexplored. In this paper, we study the correlation between optimal learning
rate, batch size, and number of training tokens for the recently proposed WSD
scheduler. After thousands of small experiments, we found a power-law
relationship between variables and demonstrated its transferability across
model sizes. Based on the observation, we propose a new learning rate
scheduler, Power scheduler, that is agnostic about the number of training
tokens and batch size. The experiment shows that combining the Power scheduler
with Maximum Update Parameterization (muP) can consistently achieve impressive
performance with one set of hyperparameters regardless of the number of
training tokens, batch size, model size, and even model architecture. Our 3B
dense and MoE models trained with the Power scheduler achieve comparable
performance as state-of-the-art small language models. We open-source these
pretrained models at https://ibm.biz/BdKhLa. | cs.CL | [
"cs.CL",
"cs.AI",
"cs.LG"
] |
|||
Disentangled Training with Adversarial Examples For Robust
Small-footprint Keyword Spotting | http://arxiv.org/abs/2408.13355v1 | http://arxiv.org/abs/2408.13355v1 | http://arxiv.org/pdf/2408.13355v1 | 2024-08-23 | 2024-08-23 | [
"Zhenyu Wang",
"Li Wan",
"Biqiao Zhang",
"Yiteng Huang",
"Shang-Wen Li",
"Ming Sun",
"Xin Lei",
"Zhaojun Yang"
] | [
"",
"",
"",
"",
"",
"",
"",
""
] | A keyword spotting (KWS) engine that is continuously running on device is
exposed to various speech signals that are usually unseen before. It is a
challenging problem to build a small-footprint and high-performing KWS model
with robustness under different acoustic environments. In this paper, we
explore how to effectively apply adversarial examples to improve KWS
robustness. We propose datasource-aware disentangled learning with adversarial
examples to reduce the mismatch between the original and adversarial data as
well as the mismatch across original training datasources. The KWS model
architecture is based on depth-wise separable convolution and a simple
attention module. Experimental results demonstrate that the proposed learning
strategy improves false reject rate by $40.31%$ at $1%$ false accept rate on
the internal dataset, compared to the strongest baseline without using
adversarial examples. Our best-performing system achieves $98.06%$ accuracy on
the Google Speech Commands V1 dataset. | ICASSP 2023 | cs.SD | [
"cs.SD",
"cs.AI",
"eess.AS"
] |
||
Toward Improving Synthetic Audio Spoofing Detection Robustness via
Meta-Learning and Disentangled Training With Adversarial Examples | http://arxiv.org/abs/2408.13341v1 | http://arxiv.org/abs/2408.13341v1 | http://arxiv.org/pdf/2408.13341v1 | 2024-08-23 | 2024-08-23 | [
"Zhenyu Wang",
"John H. L. Hansen"
] | [
"",
""
] | Advances in automatic speaker verification (ASV) promote research into the
formulation of spoofing detection systems for real-world applications. The
performance of ASV systems can be degraded severely by multiple types of
spoofing attacks, namely, synthetic speech (SS), voice conversion (VC), replay,
twins and impersonation, especially in the case of unseen synthetic spoofing
attacks. A reliable and robust spoofing detection system can act as a security
gate to filter out spoofing attacks instead of having them reach the ASV
system. A weighted additive angular margin loss is proposed to address the data
imbalance issue, and different margins has been assigned to improve
generalization to unseen spoofing attacks in this study. Meanwhile, we
incorporate a meta-learning loss function to optimize differences between the
embeddings of support versus query set in order to learn a
spoofing-category-independent embedding space for utterances. Furthermore, we
craft adversarial examples by adding imperceptible perturbations to spoofing
speech as a data augmentation strategy, then we use an auxiliary batch
normalization (BN) to guarantee that corresponding normalization statistics are
performed exclusively on the adversarial examples. Additionally, A simple
attention module is integrated into the residual block to refine the feature
extraction process. Evaluation results on the Logical Access (LA) track of the
ASVspoof 2019 corpus provides confirmation of our proposed approaches'
effectiveness in terms of a pooled EER of 0.87%, and a min t-DCF of 0.0277.
These advancements offer effective options to reduce the impact of spoofing
attacks on voice recognition/authentication systems. | IEEE ACCESS 2024 | IEEE ACCESS 2024 | 10.1109/ACCESS.2024.3421281 | cs.SD | [
"cs.SD",
"cs.AI",
"eess.AS"
] |
SHEDAD: SNN-Enhanced District Heating Anomaly Detection for Urban
Substations | http://arxiv.org/abs/2408.14499v1 | http://arxiv.org/abs/2408.14499v1 | http://arxiv.org/pdf/2408.14499v1 | 2024-08-23 | 2024-08-23 | [
"Jonne van Dreven",
"Abbas Cheddad",
"Sadi Alawadi",
"Ahmad Nauman Ghazi",
"Jad Al Koussa",
"Dirk Vanhoudt"
] | [
"",
"",
"",
"",
"",
""
] | District Heating (DH) systems are essential for energy-efficient urban
heating. However, despite the advancements in automated fault detection and
diagnosis (FDD), DH still faces challenges in operational faults that impact
efficiency. This study introduces the Shared Nearest Neighbor Enhanced District
Heating Anomaly Detection (SHEDAD) approach, designed to approximate the DH
network topology and allow for local anomaly detection without disclosing
sensitive information, such as substation locations. The approach leverages a
multi-adaptive k-Nearest Neighbor (k-NN) graph to improve the initial
neighborhood creation. Moreover, it introduces a merging technique that reduces
noise and eliminates trivial edges. We use the Median Absolute Deviation (MAD)
and modified z-scores to flag anomalous substations. The results reveal that
SHEDAD outperforms traditional clustering methods, achieving significantly
lower intra-cluster variance and distance. Additionally, SHEDAD effectively
isolates and identifies two distinct categories of anomalies: supply
temperatures and substation performance. We identified 30 anomalous substations
and reached a sensitivity of approximately 65\% and specificity of
approximately 97\%. By focusing on this subset of poor-performing substations
in the network, SHEDAD enables more targeted and effective maintenance
interventions, which can reduce energy usage while optimizing network
performance. | 12 pages, 5 figures, FMEC2024 | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
LalaEval: A Holistic Human Evaluation Framework for Domain-Specific
Large Language Models | http://arxiv.org/abs/2408.13338v1 | http://arxiv.org/abs/2408.13338v1 | http://arxiv.org/pdf/2408.13338v1 | 2024-08-23 | 2024-08-23 | [
"Chongyan Sun",
"Ken Lin",
"Shiwei Wang",
"Hulong Wu",
"Chengfei Fu",
"Zhen Wang"
] | [
"",
"",
"",
"",
"",
""
] | This paper introduces LalaEval, a holistic framework designed for the human
evaluation of domain-specific large language models (LLMs). LalaEval proposes a
comprehensive suite of end-to-end protocols that cover five main components
including domain specification, criteria establishment, benchmark dataset
creation, construction of evaluation rubrics, and thorough analysis and
interpretation of evaluation outcomes. This initiative aims to fill a crucial
research gap by providing a systematic methodology for conducting standardized
human evaluations within specific domains, a practice that, despite its
widespread application, lacks substantial coverage in the literature and human
evaluation are often criticized to be less reliable due to subjective factors,
so standardized procedures adapted to the nuanced requirements of specific
domains or even individual organizations are in great need. Furthermore, the
paper demonstrates the framework's application within the logistics industry,
presenting domain-specific evaluation benchmarks, datasets, and a comparative
analysis of LLMs for the logistics domain use, highlighting the framework's
capacity to elucidate performance differences and guide model selection and
development for domain-specific LLMs. Through real-world deployment, the paper
underscores the framework's effectiveness in advancing the field of
domain-specific LLM evaluation, thereby contributing significantly to the
ongoing discussion on LLMs' practical utility and performance in
domain-specific applications. | cs.HC | [
"cs.HC",
"cs.AI",
"cs.CL"
] |
|||
Mastering the Digital Art of War: Developing Intelligent Combat
Simulation Agents for Wargaming Using Hierarchical Reinforcement Learning | http://arxiv.org/abs/2408.13333v1 | http://arxiv.org/abs/2408.13333v1 | http://arxiv.org/pdf/2408.13333v1 | 2024-08-23 | 2024-08-23 | [
"Scotty Black"
] | [
""
] | In today's rapidly evolving military landscape, advancing artificial
intelligence (AI) in support of wargaming becomes essential. Despite
reinforcement learning (RL) showing promise for developing intelligent agents,
conventional RL faces limitations in handling the complexity inherent in combat
simulations. This dissertation proposes a comprehensive approach, including
targeted observation abstractions, multi-model integration, a hybrid AI
framework, and an overarching hierarchical reinforcement learning (HRL)
framework. Our localized observation abstraction using piecewise linear spatial
decay simplifies the RL problem, enhancing computational efficiency and
demonstrating superior efficacy over traditional global observation methods.
Our multi-model framework combines various AI methodologies, optimizing
performance while still enabling the use of diverse, specialized individual
behavior models. Our hybrid AI framework synergizes RL with scripted agents,
leveraging RL for high-level decisions and scripted agents for lower-level
tasks, enhancing adaptability, reliability, and performance. Our HRL
architecture and training framework decomposes complex problems into manageable
subproblems, aligning with military decision-making structures. Although
initial tests did not show improved performance, insights were gained to
improve future iterations. This study underscores AI's potential to
revolutionize wargaming, emphasizing the need for continued research in this
domain. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
Localized Observation Abstraction Using Piecewise Linear Spatial Decay
for Reinforcement Learning in Combat Simulations | http://arxiv.org/abs/2408.13328v1 | http://arxiv.org/abs/2408.13328v1 | http://arxiv.org/pdf/2408.13328v1 | 2024-08-23 | 2024-08-23 | [
"Scotty Black",
"Christian Darken"
] | [
"",
""
] | In the domain of combat simulations, the training and deployment of deep
reinforcement learning (RL) agents still face substantial challenges due to the
dynamic and intricate nature of such environments. Unfortunately, as the
complexity of the scenarios and available information increases, the training
time required to achieve a certain threshold of performance does not just
increase, but often does so exponentially. This relationship underscores the
profound impact of complexity in training RL agents. This paper introduces a
novel approach that addresses this limitation in training artificial
intelligence (AI) agents using RL. Traditional RL methods have been shown to
struggle in these high-dimensional, dynamic environments due to real-world
computational constraints and the known sample inefficiency challenges of RL.
To overcome these limitations, we propose a method of localized observation
abstraction using piecewise linear spatial decay. This technique simplifies the
state space, reducing computational demands while still preserving essential
information, thereby enhancing AI training efficiency in dynamic environments
where spatial relationships are often critical. Our analysis reveals that this
localized observation approach consistently outperforms the more traditional
global observation approach across increasing scenario complexity levels. This
paper advances the research on observation abstractions for RL, illustrating
how localized observation with piecewise linear spatial decay can provide an
effective solution to large state representation challenges in dynamic
environments. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
How Diffusion Models Learn to Factorize and Compose | http://arxiv.org/abs/2408.13256v1 | http://arxiv.org/abs/2408.13256v1 | http://arxiv.org/pdf/2408.13256v1 | 2024-08-23 | 2024-08-23 | [
"Qiyao Liang",
"Ziming Liu",
"Mitchell Ostrow",
"Ila Fiete"
] | [
"",
"",
"",
""
] | Diffusion models are capable of generating photo-realistic images that
combine elements which likely do not appear together in the training set,
demonstrating the ability to compositionally generalize. Nonetheless, the
precise mechanism of compositionality and how it is acquired through training
remains elusive. Inspired by cognitive neuroscientific approaches, we consider
a highly reduced setting to examine whether and when diffusion models learn
semantically meaningful and factorized representations of composable features.
We performed extensive controlled experiments on conditional Denoising
Diffusion Probabilistic Models (DDPMs) trained to generate various forms of 2D
Gaussian data. We found that the models learn factorized but not fully
continuous manifold representations for encoding continuous features of
variation underlying the data. With such representations, models demonstrate
superior feature compositionality but limited ability to interpolate over
unseen values of a given feature. Our experimental results further demonstrate
that diffusion models can attain compositionality with few compositional
examples, suggesting a more efficient way to train DDPMs. Finally, we connect
manifold formation in diffusion models to percolation theory in physics,
offering insight into the sudden onset of factorized representation learning.
Our thorough toy experiments thus contribute a deeper understanding of how
diffusion models capture compositional structure in data. | 11 pages, 6 figures, plus appendix, some content overlap with
arXiv:2402.03305 | cs.AI | [
"cs.AI",
"cs.CV",
"cs.LG"
] |
||
Ensemble Modeling of Multiple Physical Indicators to Dynamically
Phenotype Autism Spectrum Disorder | http://arxiv.org/abs/2408.13255v1 | http://arxiv.org/abs/2408.13255v1 | http://arxiv.org/pdf/2408.13255v1 | 2024-08-23 | 2024-08-23 | [
"Marie Huynh",
"Aaron Kline",
"Saimourya Surabhi",
"Kaitlyn Dunlap",
"Onur Cezmi Mutlu",
"Mohammadmahdi Honarmand",
"Parnian Azizian",
"Peter Washington",
"Dennis P. Wall"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
""
] | Early detection of autism, a neurodevelopmental disorder marked by social
communication challenges, is crucial for timely intervention. Recent
advancements have utilized naturalistic home videos captured via the mobile
application GuessWhat. Through interactive games played between children and
their guardians, GuessWhat has amassed over 3,000 structured videos from 382
children, both diagnosed with and without Autism Spectrum Disorder (ASD). This
collection provides a robust dataset for training computer vision models to
detect ASD-related phenotypic markers, including variations in emotional
expression, eye contact, and head movements. We have developed a protocol to
curate high-quality videos from this dataset, forming a comprehensive training
set. Utilizing this set, we trained individual LSTM-based models using eye
gaze, head positions, and facial landmarks as input features, achieving test
AUCs of 86%, 67%, and 78%, respectively. To boost diagnostic accuracy, we
applied late fusion techniques to create ensemble models, improving the overall
AUC to 90%. This approach also yielded more equitable results across different
genders and age groups. Our methodology offers a significant step forward in
the early detection of ASD by potentially reducing the reliance on subjective
assessments and making early identification more accessibly and equitable. | cs.CV | [
"cs.CV",
"cs.AI"
] |
|||
Foundational Model for Electron Micrograph Analysis: Instruction-Tuning
Small-Scale Language-and-Vision Assistant for Enterprise Adoption | http://arxiv.org/abs/2408.13248v1 | http://arxiv.org/abs/2408.13248v1 | http://arxiv.org/pdf/2408.13248v1 | 2024-08-23 | 2024-08-23 | [
"Sakhinana Sagar Srinivas",
"Chidaksh Ravuru",
"Geethan Sannidhi",
"Venkataramana Runkana"
] | [
"",
"",
"",
""
] | Semiconductor imaging and analysis are critical yet understudied in deep
learning, limiting our ability for precise control and optimization in
semiconductor manufacturing. We introduce a small-scale multimodal framework
for analyzing semiconductor electron microscopy images (MAEMI) through
vision-language instruction tuning. We generate a customized
instruction-following dataset using large multimodal models on microscopic
image analysis. We perform knowledge transfer from larger to smaller models
through knowledge distillation, resulting in improved accuracy of smaller
models on visual question answering (VQA) tasks. This approach eliminates the
need for expensive, human expert-annotated datasets for microscopic image
analysis tasks. Enterprises can further finetune MAEMI on their intellectual
data, enhancing privacy and performance on low-cost consumer hardware. Our
experiments show that MAEMI outperforms traditional methods, adapts to data
distribution shifts, and supports high-throughput screening. | Our paper is published at ICML 2024 Workshop ML for Life and Material
Science: From Theory to Industry Applications, Vienna, Austria | cs.CV | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
||
Data Exposure from LLM Apps: An In-depth Investigation of OpenAI's GPTs | http://arxiv.org/abs/2408.13247v1 | http://arxiv.org/abs/2408.13247v1 | http://arxiv.org/pdf/2408.13247v1 | 2024-08-23 | 2024-08-23 | [
"Evin Jaff",
"Yuhao Wu",
"Ning Zhang",
"Umar Iqbal"
] | [
"",
"",
"",
""
] | LLM app ecosystems are quickly maturing and supporting a wide range of use
cases, which requires them to collect excessive user data. Given that the LLM
apps are developed by third-parties and that anecdotal evidence suggests LLM
platforms currently do not strictly enforce their policies, user data shared
with arbitrary third-parties poses a significant privacy risk. In this paper we
aim to bring transparency in data practices of LLM apps. As a case study, we
study OpenAI's GPT app ecosystem. We develop an LLM-based framework to conduct
the static analysis of natural language-based source code of GPTs and their
Actions (external services) to characterize their data collection practices.
Our findings indicate that Actions collect expansive data about users,
including sensitive information prohibited by OpenAI, such as passwords. We
find that some Actions, including related to advertising and analytics, are
embedded in multiple GPTs, which allow them to track user activities across
GPTs. Additionally, co-occurrence of Actions exposes as much as 9.5x more data
to them, than it is exposed to individual Actions. Lastly, we develop an
LLM-based privacy policy analysis framework to automatically check the
consistency of data collection by Actions with disclosures in their privacy
policies. Our measurements indicate that the disclosures for most of the
collected data types are omitted in privacy policies, with only 5.8% of Actions
clearly disclosing their data collection practices. | cs.CR | [
"cs.CR",
"cs.AI",
"cs.CL",
"cs.CY",
"cs.LG"
] |
|||
JacNet: Learning Functions with Structured Jacobians | http://arxiv.org/abs/2408.13237v1 | http://arxiv.org/abs/2408.13237v1 | http://arxiv.org/pdf/2408.13237v1 | 2024-08-23 | 2024-08-23 | [
"Jonathan Lorraine",
"Safwan Hossain"
] | [
"",
""
] | Neural networks are trained to learn an approximate mapping from an input
domain to a target domain. Incorporating prior knowledge about true mappings is
critical to learning a useful approximation. With current architectures, it is
challenging to enforce structure on the derivatives of the input-output
mapping. We propose to use a neural network to directly learn the Jacobian of
the input-output function, which allows easy control of the derivative. We
focus on structuring the derivative to allow invertibility and also demonstrate
that other useful priors, such as $k$-Lipschitz, can be enforced. Using this
approach, we can learn approximations to simple functions that are guaranteed
to be invertible and easily compute the inverse. We also show similar results
for 1-Lipschitz functions. | 6 pages, 3 Figures, ICML 2019 INNF Workshop | cs.LG | [
"cs.LG",
"cs.AI",
"stat.ML",
"68T07",
"I.2.6; G.1.0; I.5.1"
] |
||
Multi-Layer Transformers Gradient Can be Approximated in Almost Linear
Time | http://arxiv.org/abs/2408.13233v1 | http://arxiv.org/abs/2408.13233v1 | http://arxiv.org/pdf/2408.13233v1 | 2024-08-23 | 2024-08-23 | [
"Yingyu Liang",
"Zhizhou Sha",
"Zhenmei Shi",
"Zhao Song",
"Yufa Zhou"
] | [
"",
"",
"",
"",
""
] | The quadratic computational complexity in the self-attention mechanism of
popular transformer architectures poses significant challenges for training and
inference, particularly in terms of efficiency and memory requirements. Towards
addressing these challenges, this paper introduces a novel fast computation
method for gradient calculation in multi-layer transformer models. Our approach
enables the computation of gradients for the entire multi-layer transformer
model in almost linear time $n^{1+o(1)}$, where $n$ is the input sequence
length. This breakthrough significantly reduces the computational bottleneck
associated with the traditional quadratic time complexity. Our theory holds for
any loss function and maintains a bounded approximation error across the entire
model. Furthermore, our analysis can hold when the multi-layer transformer
model contains many practical sub-modules, such as residual connection, casual
mask, and multi-head attention. By improving the efficiency of gradient
computation in large language models, we hope that our work will facilitate the
more effective training and deployment of long-context language models based on
our theoretical results. | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CL"
] |
|||
Enhancing Few-Shot Transfer Learning with Optimized Multi-Task Prompt
Tuning through Modular Prompt Composition | http://arxiv.org/abs/2408.13227v1 | http://arxiv.org/abs/2408.13227v1 | http://arxiv.org/pdf/2408.13227v1 | 2024-08-23 | 2024-08-23 | [
"Ahmad Pouramini",
"Hesham Faili"
] | [
"",
""
] | In recent years, multi-task prompt tuning has garnered considerable attention
for its inherent modularity and potential to enhance parameter-efficient
transfer learning across diverse tasks. This paper aims to analyze and improve
the performance of multiple tasks by facilitating the transfer of knowledge
between their corresponding prompts in a multi-task setting. Our proposed
approach decomposes the prompt for each target task into a combination of
shared prompts (source prompts) and a task-specific prompt (private prompt).
During training, the source prompts undergo fine-tuning and are integrated with
the private prompt to drive the target prompt for each task. We present and
compare multiple methods for combining source prompts to construct the target
prompt, analyzing the roles of both source and private prompts within each
method. We investigate their contributions to task performance and offer
flexible, adjustable configurations based on these insights to optimize
performance. Our empirical findings clearly showcase improvements in accuracy
and robustness compared to the conventional practice of prompt tuning and
related works. Notably, our results substantially outperform other methods in
the field in few-shot settings, demonstrating superior performance in various
tasks across GLUE benchmark, among other tasks. This achievement is attained
with a significantly reduced amount of training data, making our method a
promising one for few-shot settings. | cs.AI | [
"cs.AI",
"cs.CL"
] |
|||
HBIC: A Biclustering Algorithm for Heterogeneous Datasets | http://arxiv.org/abs/2408.13217v1 | http://arxiv.org/abs/2408.13217v1 | http://arxiv.org/pdf/2408.13217v1 | 2024-08-23 | 2024-08-23 | [
"Adán José-García",
"Julie Jacques",
"Clément Chauvet",
"Vincent Sobanski",
"Clarisse Dhaenens"
] | [
"",
"",
"",
"",
""
] | Biclustering is an unsupervised machine-learning approach aiming to cluster
rows and columns simultaneously in a data matrix. Several biclustering
algorithms have been proposed for handling numeric datasets. However,
real-world data mining problems often involve heterogeneous datasets with mixed
attributes. To address this challenge, we introduce a biclustering approach
called HBIC, capable of discovering meaningful biclusters in complex
heterogeneous data, including numeric, binary, and categorical data. The
approach comprises two stages: bicluster generation and bicluster model
selection. In the initial stage, several candidate biclusters are generated
iteratively by adding and removing rows and columns based on the frequency of
values in the original matrix. In the second stage, we introduce two approaches
for selecting the most suitable biclusters by considering their size and
homogeneity. Through a series of experiments, we investigated the suitability
of our approach on a synthetic benchmark and in a biomedical application
involving clinical data of systemic sclerosis patients. The evaluation
comparing our method to existing approaches demonstrates its ability to
discover high-quality biclusters from heterogeneous data. Our biclustering
approach is a starting point for heterogeneous bicluster discovery, leading to
a better understanding of complex underlying data structures. | 11 pages, 5 figures | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
EUR-USD Exchange Rate Forecasting Based on Information Fusion with Large
Language Models and Deep Learning Methods | http://arxiv.org/abs/2408.13214v1 | http://arxiv.org/abs/2408.13214v1 | http://arxiv.org/pdf/2408.13214v1 | 2024-08-23 | 2024-08-23 | [
"Hongcheng Ding",
"Xuanze Zhao",
"Zixiao Jiang",
"Shamsul Nahar Abdullah",
"Deshinta Arrova Dewi"
] | [
"",
"",
"",
"",
""
] | Accurate forecasting of the EUR/USD exchange rate is crucial for investors,
businesses, and policymakers. This paper proposes a novel framework, IUS, that
integrates unstructured textual data from news and analysis with structured
data on exchange rates and financial indicators to enhance exchange rate
prediction. The IUS framework employs large language models for sentiment
polarity scoring and exchange rate movement classification of texts. These
textual features are combined with quantitative features and input into a
Causality-Driven Feature Generator. An Optuna-optimized Bi-LSTM model is then
used to forecast the EUR/USD exchange rate. Experiments demonstrate that the
proposed method outperforms benchmark models, reducing MAE by 10.69% and RMSE
by 9.56% compared to the best performing baseline. Results also show the
benefits of data fusion, with the combination of unstructured and structured
data yielding higher accuracy than structured data alone. Furthermore, feature
selection using the top 12 important quantitative features combined with the
textual features proves most effective. The proposed IUS framework and
Optuna-Bi-LSTM model provide a powerful new approach for exchange rate
forecasting through multi-source data integration. | q-fin.CP | [
"q-fin.CP",
"cs.AI",
"cs.CE",
"cs.CL"
] |
|||
Optimal Quantum Circuit Design via Unitary Neural Networks | http://arxiv.org/abs/2408.13211v1 | http://arxiv.org/abs/2408.13211v1 | http://arxiv.org/pdf/2408.13211v1 | 2024-08-23 | 2024-08-23 | [
"M. Zomorodi",
"H. Amini",
"M. Abbaszadeh",
"J. Sohrabi",
"V. Salari",
"P. Plawiak"
] | [
"",
"",
"",
"",
"",
""
] | The process of translating a quantum algorithm into a form suitable for
implementation on a quantum computing platform is crucial but yet challenging.
This entails specifying quantum operations with precision, a typically
intricate task. In this paper, we present an alternative approach: an automated
method for synthesizing the functionality of a quantum algorithm into a quantum
circuit model representation. Our methodology involves training a neural
network model using diverse input-output mappings of the quantum algorithm. We
demonstrate that this trained model can effectively generate a quantum circuit
model equivalent to the original algorithm. Remarkably, our observations
indicate that the trained model achieves near-perfect mapping of unseen inputs
to their respective outputs. | quant-ph | [
"quant-ph",
"cs.AI"
] |
|||
Temporal Fairness in Decision Making Problems | http://arxiv.org/abs/2408.13208v1 | http://arxiv.org/abs/2408.13208v1 | http://arxiv.org/pdf/2408.13208v1 | 2024-08-23 | 2024-08-23 | [
"Manuel R. Torres",
"Parisa Zehtabi",
"Michael Cashmore",
"Daniele Magazzeni",
"Manuela Veloso"
] | [
"",
"",
"",
"",
""
] | In this work we consider a new interpretation of fairness in decision making
problems. Building upon existing fairness formulations, we focus on how to
reason over fairness from a temporal perspective, taking into account the
fairness of a history of past decisions. After introducing the concept of
temporal fairness, we propose three approaches that incorporate temporal
fairness in decision making problems formulated as optimization problems. We
present a qualitative evaluation of our approach in four different domains and
compare the solutions against a baseline approach that does not consider the
temporal aspect of fairness. | Paper accepted at ECAI 2024. This is an extended version that
includes Supplementary Material | cs.AI | [
"cs.AI"
] |
||
DOMAINEVAL: An Auto-Constructed Benchmark for Multi-Domain Code
Generation | http://arxiv.org/abs/2408.13204v1 | http://arxiv.org/abs/2408.13204v1 | http://arxiv.org/pdf/2408.13204v1 | 2024-08-23 | 2024-08-23 | [
"Qiming Zhu",
"Jialun Cao",
"Yaojie Lu",
"Hongyu Lin",
"Xianpei Han",
"Le Sun",
"Shing-Chi Cheung"
] | [
"",
"",
"",
"",
"",
"",
""
] | Code benchmarks such as HumanEval are widely adopted to evaluate the
capabilities of Large Language Models (LLMs), providing insights into their
strengths and weaknesses. However, current benchmarks primarily exercise LLMs'
capability on common coding tasks (e.g., bubble sort, greatest common divisor),
leaving domain-specific coding tasks (e.g., computation, system, cryptography)
unexplored. To fill this gap, we propose a multi-domain code benchmark,
DOMAINEVAL, designed to evaluate LLMs' coding capabilities thoroughly. Our
pipeline works in a fully automated manner, enabling a push-bottom construction
from code repositories into formatted subjects under study. Interesting
findings are observed by evaluating 12 representative LLMs against DOMAINEVAL.
We notice that LLMs are generally good at computation tasks while falling short
on cryptography and system coding tasks. The performance gap can be as much as
68.94% (80.94% - 12.0%) in some LLMs. We also observe that generating more
samples can increase the overall performance of LLMs, while the domain bias may
even increase. The contributions of this study include a code generation
benchmark dataset DOMAINEVAL, encompassing six popular domains, a fully
automated pipeline for constructing code benchmarks, and an identification of
the limitations of LLMs in code generation tasks based on their performance on
DOMAINEVAL, providing directions for future research improvements. The
leaderboard is available at https://domaineval.github.io/. | cs.AI | [
"cs.AI",
"cs.SE"
] |
|||
A New Era in Computational Pathology: A Survey on Foundation and
Vision-Language Models | http://arxiv.org/abs/2408.14496v1 | http://arxiv.org/abs/2408.14496v1 | http://arxiv.org/pdf/2408.14496v1 | 2024-08-23 | 2024-08-23 | [
"Dibaloke Chanda",
"Milan Aryal",
"Nasim Yahya Soltani",
"Masoud Ganji"
] | [
"",
"",
"",
""
] | Recent advances in deep learning have completely transformed the domain of
computational pathology (CPath), which in turn altered the diagnostic workflow
of pathologists by integrating foundation models (FMs) and vision-language
models (VLMs) in their assessment and decision-making process. FMs overcome the
limitations of existing deep learning approaches in CPath by learning a
representation space that can be adapted to a wide variety of downstream tasks
without explicit supervision. VLMs allow pathology reports written in natural
language to be used as a rich semantic information source to improve existing
models as well as generate predictions in natural language form. In this
survey, a holistic and systematic overview of recent innovations in FMs and
VLMs in CPath is presented. Furthermore, the tools, datasets and training
schemes for these models are summarized in addition to categorizing them into
distinct groups. This extensive survey highlights the current trends in CPath
and the way it is going to be transformed through FMs and VLMs in the future. | Initial Version | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CL",
"eess.IV"
] |
||
Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis
on Textual Reviews | http://arxiv.org/abs/2408.13202v1 | http://arxiv.org/abs/2408.13202v1 | http://arxiv.org/pdf/2408.13202v1 | 2024-08-23 | 2024-08-23 | [
"Dineth Jayakody",
"A V A Malkith",
"Koshila Isuranda",
"Vishal Thenuwara",
"Nisansa de Silva",
"Sachintha Rajith Ponnamperuma",
"G G N Sandamali",
"K L K Sudheera"
] | [
"",
"",
"",
"",
"",
"",
"",
""
] | Aspect-based Sentiment Analysis (ABSA) is a critical task in Natural Language
Processing (NLP) that focuses on extracting sentiments related to specific
aspects within a text, offering deep insights into customer opinions.
Traditional sentiment analysis methods, while useful for determining overall
sentiment, often miss the implicit opinions about particular product or service
features. This paper presents a comprehensive review of the evolution of ABSA
methodologies, from lexicon-based approaches to machine learning and deep
learning techniques. We emphasize the recent advancements in Transformer-based
models, particularly Bidirectional Encoder Representations from Transformers
(BERT) and its variants, which have set new benchmarks in ABSA tasks. We
focused on finetuning Llama and Mistral models, building hybrid models using
the SetFit framework, and developing our own model by exploiting the strengths
of state-of-the-art (SOTA) Transformer-based models for aspect term extraction
(ATE) and aspect sentiment classification (ASC). Our hybrid model Instruct -
DeBERTa uses SOTA InstructABSA for aspect extraction and DeBERTa-V3-baseabsa-V1
for aspect sentiment classification. We utilize datasets from different domains
to evaluate our model's performance. Our experiments indicate that the proposed
hybrid model significantly improves the accuracy and reliability of sentiment
analysis across all experimented domains. As per our findings, our hybrid model
Instruct - DeBERTa is the best-performing model for the joint task of ATE and
ASC for both SemEval restaurant 2014 and SemEval laptop 2014 datasets
separately. By addressing the limitations of existing methodologies, our
approach provides a robust solution for understanding detailed consumer
feedback, thus offering valuable insights for businesses aiming to enhance
customer satisfaction and product development. | cs.CL | [
"cs.CL",
"cs.AI"
] |
|||
An Overview and Comparison of Axiomatization Structures Regarding
Inconsistency Indices' Properties in Pairwise Comparisons Methods | http://arxiv.org/abs/2408.13297v1 | http://arxiv.org/abs/2408.13297v1 | http://arxiv.org/pdf/2408.13297v1 | 2024-08-23 | 2024-08-23 | [
"Sangeeta Pant",
"Anuj Kumar",
"Jiří Mazurek"
] | [
"",
"",
""
] | Mathematical analysis of the analytic hierarchy process (AHP) led to the
development of a mathematical function, usually called the inconsistency index,
which has the center role in measuring the inconsistency of the judgements in
AHP. Inconsistency index is a mathematical function which maps every pairwise
comparison matrix (PCM) into a real number. An inconsistency index can be
considered more trustworthy when it satisfies a set of suitable properties.
Therefore, the research community has been trying to postulate a set of
desirable rules (axioms, properties) for inconsistency indices. Subsequently,
many axiomatic frameworks for these functions have been suggested
independently, however, the literature on the topic is fragmented and missing a
broader framework. Therefore, the objective of this article is twofold.
Firstly, we provide a comprehensive review of the advancements in the
axiomatization of inconsistency indices' properties during the last decade.
Secondly, we provide a comparison and discussion of the aforementioned
axiomatic structures along with directions of the future research. | 21 pages, 2 figures | cs.LO | [
"cs.LO",
"cs.AI"
] |
||
Accelerating the k-means++ Algorithm by Using Geometric Information | http://arxiv.org/abs/2408.13189v1 | http://arxiv.org/abs/2408.13189v1 | http://arxiv.org/pdf/2408.13189v1 | 2024-08-23 | 2024-08-23 | [
"Guillem Rodríguez Corominas",
"Maria J. Blesa",
"Christian Blum"
] | [
"",
"",
""
] | In this paper, we propose an acceleration of the exact k-means++ algorithm
using geometric information, specifically the Triangle Inequality and
additional norm filters, along with a two-step sampling procedure. Our
experiments demonstrate that the accelerated version outperforms the standard
k-means++ version in terms of the number of visited points and distance
calculations, achieving greater speedup as the number of clusters increases.
The version utilizing the Triangle Inequality is particularly effective for
low-dimensional data, while the additional norm-based filter enhances
performance in high-dimensional instances with greater norm variance among
points. Additional experiments show the behavior of our algorithms when
executed concurrently across multiple jobs and examine how memory performance
impacts practical speedup. | cs.LG | [
"cs.LG",
"cs.AI",
"91C20"
] |
|||
Say No to Freeloader: Protecting Intellectual Property of Your Deep
Model | http://arxiv.org/abs/2408.13161v2 | http://arxiv.org/abs/2408.13161v2 | http://arxiv.org/pdf/2408.13161v2 | 2024-08-23 | 2024-08-27 | [
"Lianyu Wang",
"Meng Wang",
"Huazhu Fu",
"Daoqiang Zhang"
] | [
"",
"",
"",
""
] | Model intellectual property (IP) protection has attracted growing attention
as science and technology advancements stem from human intellectual labor and
computational expenses. Ensuring IP safety for trainers and owners is of utmost
importance, particularly in domains where ownership verification and
applicability authorization are required. A notable approach to safeguarding
model IP involves proactively preventing the use of well-trained models of
authorized domains from unauthorized domains. In this paper, we introduce a
novel Compact Un-transferable Pyramid Isolation Domain (CUPI-Domain) which
serves as a barrier against illegal transfers from authorized to unauthorized
domains. Drawing inspiration from human transitive inference and learning
abilities, the CUPI-Domain is designed to obstruct cross-domain transfers by
emphasizing the distinctive style features of the authorized domain. This
emphasis leads to failure in recognizing irrelevant private style features on
unauthorized domains. To this end, we propose novel CUPI-Domain generators,
which select features from both authorized and CUPI-Domain as anchors. Then, we
fuse the style features and semantic features of these anchors to generate
labeled and style-rich CUPI-Domain. Additionally, we design external
Domain-Information Memory Banks (DIMB) for storing and updating labeled pyramid
features to obtain stable domain class features and domain class-wise style
features. Based on the proposed whole method, the novel style and
discriminative loss functions are designed to effectively enhance the
distinction in style and discriminative features between authorized and
unauthorized domains, respectively. Moreover, we provide two solutions for
utilizing CUPI-Domain based on whether the unauthorized domain is known:
target-specified CUPI-Domain and target-free CUPI-Domain. | cs.AI | [
"cs.AI"
] |
|||
Causal machine learning for sustainable agroecosystems | http://arxiv.org/abs/2408.13155v1 | http://arxiv.org/abs/2408.13155v1 | http://arxiv.org/pdf/2408.13155v1 | 2024-08-23 | 2024-08-23 | [
"Vasileios Sitokonstantinou",
"Emiliano Díaz Salas Porras",
"Jordi Cerdà Bautista",
"Maria Piles",
"Ioannis Athanasiadis",
"Hannah Kerner",
"Giulia Martini",
"Lily-belle Sweet",
"Ilias Tsoumas",
"Jakob Zscheischler",
"Gustau Camps-Valls"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
""
] | In a changing climate, sustainable agriculture is essential for food security
and environmental health. However, it is challenging to understand the complex
interactions among its biophysical, social, and economic components. Predictive
machine learning (ML), with its capacity to learn from data, is leveraged in
sustainable agriculture for applications like yield prediction and weather
forecasting. Nevertheless, it cannot explain causal mechanisms and remains
descriptive rather than prescriptive. To address this gap, we propose causal
ML, which merges ML's data processing with causality's ability to reason about
change. This facilitates quantifying intervention impacts for evidence-based
decision-making and enhances predictive model robustness. We showcase causal ML
through eight diverse applications that benefit stakeholders across the
agri-food chain, including farmers, policymakers, and researchers. | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CY"
] |
|||
ShapeICP: Iterative Category-level Object Pose and Shape Estimation from
Depth | http://arxiv.org/abs/2408.13147v1 | http://arxiv.org/abs/2408.13147v1 | http://arxiv.org/pdf/2408.13147v1 | 2024-08-23 | 2024-08-23 | [
"Yihao Zhang",
"John J. Leonard"
] | [
"",
""
] | Category-level object pose and shape estimation from a single depth image has
recently drawn research attention due to its wide applications in robotics and
self-driving. The task is particularly challenging because the three unknowns,
object pose, object shape, and model-to-measurement correspondences, are
compounded together but only a single view of depth measurements is provided.
The vast majority of the prior work heavily relies on data-driven approaches to
obtain solutions to at least one of the unknowns and typically two, running
with the risk of failing to generalize to unseen domains. The shape
representations used in the prior work also mainly focus on point cloud and
signed distance field (SDF). In stark contrast to the prior work, we approach
the problem using an iterative estimation method that does not require learning
from any pose-annotated data. In addition, we adopt a novel mesh-based object
active shape model that has not been explored by the previous literature. Our
algorithm, named ShapeICP, has its foundation in the iterative closest point
(ICP) algorithm but is equipped with additional features for the category-level
pose and shape estimation task. The results show that even without using any
pose-annotated data, ShapeICP surpasses many data-driven approaches that rely
on the pose data for training, opening up new solution space for researchers to
consider. | cs.CV | [
"cs.CV",
"cs.AI",
"cs.RO"
] |
|||
Verification of Geometric Robustness of Neural Networks via Piecewise
Linear Approximation and Lipschitz Optimisation | http://arxiv.org/abs/2408.13140v2 | http://arxiv.org/abs/2408.13140v2 | http://arxiv.org/pdf/2408.13140v2 | 2024-08-23 | 2024-08-29 | [
"Ben Batten",
"Yang Zheng",
"Alessandro De Palma",
"Panagiotis Kouvaros",
"Alessio Lomuscio"
] | [
"",
"",
"",
"",
""
] | We address the problem of verifying neural networks against geometric
transformations of the input image, including rotation, scaling, shearing, and
translation. The proposed method computes provably sound piecewise linear
constraints for the pixel values by using sampling and linear approximations in
combination with branch-and-bound Lipschitz optimisation. The method obtains
provably tighter over-approximations of the perturbation region than the
present state-of-the-art. We report results from experiments on a comprehensive
set of verification benchmarks on MNIST and CIFAR10. We show that our proposed
implementation resolves up to 32% more verification cases than present
approaches. | ECAI 2024 | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CV"
] |
||
Deep Learning at the Intersection: Certified Robustness as a Tool for 3D
Vision | http://arxiv.org/abs/2408.13135v1 | http://arxiv.org/abs/2408.13135v1 | http://arxiv.org/pdf/2408.13135v1 | 2024-08-23 | 2024-08-23 | [
"Gabriel Pérez S",
"Juan C. Pérez",
"Motasem Alfarra",
"Jesús Zarzar",
"Sara Rojas",
"Bernard Ghanem",
"Pablo Arbeláez"
] | [
"",
"",
"",
"",
"",
"",
""
] | This paper presents preliminary work on a novel connection between certified
robustness in machine learning and the modeling of 3D objects. We highlight an
intriguing link between the Maximal Certified Radius (MCR) of a classifier
representing a space's occupancy and the space's Signed Distance Function
(SDF). Leveraging this relationship, we propose to use the certification method
of randomized smoothing (RS) to compute SDFs. Since RS' high computational cost
prevents its practical usage as a way to compute SDFs, we propose an algorithm
to efficiently run RS in low-dimensional applications, such as 3D space, by
expressing RS' fundamental operations as Gaussian smoothing on pre-computed
voxel grids. Our approach offers an innovative and practical tool to compute
SDFs, validated through proof-of-concept experiments in novel view synthesis.
This paper bridges two previously disparate areas of machine learning, opening
new avenues for further exploration and potential cross-domain advancements. | This paper is an accepted extended abstract to the LatinX workshop at
ICCV 2023. This was uploaded a year late | cs.CV | [
"cs.CV",
"cs.AI"
] |
||
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event
Prediction | http://arxiv.org/abs/2408.13131v1 | http://arxiv.org/abs/2408.13131v1 | http://arxiv.org/pdf/2408.13131v1 | 2024-08-23 | 2024-08-23 | [
"Ivan Karpukhin",
"Andrey Savchenko"
] | [
"",
""
] | Forecasting future events over extended periods, known as long-horizon
prediction, is a fundamental task in various domains, including retail,
finance, healthcare, and social networks. Traditional methods, such as Marked
Temporal Point Processes (MTPP), typically use autoregressive models to predict
multiple future events. However, these models frequently encounter issues such
as converging to constant or repetitive outputs, which significantly limits
their effectiveness and applicability. To overcome these limitations, we
propose DeTPP (Detection-based Temporal Point Processes), a novel approach
inspired by object detection methods from computer vision. DeTPP utilizes a
novel matching-based loss function that selectively focuses on reliably
predictable events, enhancing both training robustness and inference diversity.
Our method sets a new state-of-the-art in long-horizon event prediction,
significantly outperforming existing MTPP and next-K approaches. The
implementation of DeTPP is publicly available on GitHub. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
Causally-Aware Spatio-Temporal Multi-Graph Convolution Network for
Accurate and Reliable Traffic Prediction | http://arxiv.org/abs/2408.13293v1 | http://arxiv.org/abs/2408.13293v1 | http://arxiv.org/pdf/2408.13293v1 | 2024-08-23 | 2024-08-23 | [
"Pingping Dong",
"Xiao-Lin Wang",
"Indranil Bose",
"Kam K. H. Ng",
"Xiaoning Zhang",
"Xiaoge Zhang"
] | [
"",
"",
"",
"",
"",
""
] | Accurate and reliable prediction has profound implications to a wide range of
applications. In this study, we focus on an instance of spatio-temporal
learning problem--traffic prediction--to demonstrate an advanced deep learning
model developed for making accurate and reliable forecast. Despite the
significant progress in traffic prediction, limited studies have incorporated
both explicit and implicit traffic patterns simultaneously to improve
prediction performance. Meanwhile, the variability nature of traffic states
necessitates quantifying the uncertainty of model predictions in a
statistically principled way; however, extant studies offer no provable
guarantee on the statistical validity of confidence intervals in reflecting its
actual likelihood of containing the ground truth. In this paper, we propose an
end-to-end traffic prediction framework that leverages three primary components
to generate accurate and reliable traffic predictions: dynamic causal structure
learning for discovering implicit traffic patterns from massive traffic data,
causally-aware spatio-temporal multi-graph convolution network (CASTMGCN) for
learning spatio-temporal dependencies, and conformal prediction for uncertainty
quantification. CASTMGCN fuses several graphs that characterize different
important aspects of traffic networks and an auxiliary graph that captures the
effect of exogenous factors on the road network. On this basis, a conformal
prediction approach tailored to spatio-temporal data is further developed for
quantifying the uncertainty in node-wise traffic predictions over varying
prediction horizons. Experimental results on two real-world traffic datasets
demonstrate that the proposed method outperforms several state-of-the-art
models in prediction accuracy; moreover, it generates more efficient prediction
regions than other methods while strictly satisfying the statistical validity
in coverage. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
Map-Free Visual Relocalization Enhanced by Instance Knowledge and Depth
Knowledge | http://arxiv.org/abs/2408.13085v1 | http://arxiv.org/abs/2408.13085v1 | http://arxiv.org/pdf/2408.13085v1 | 2024-08-23 | 2024-08-23 | [
"Mingyu Xiao",
"Runze Chen",
"Haiyong Luo",
"Fang Zhao",
"Juan Wang",
"Xuepeng Ma"
] | [
"",
"",
"",
"",
"",
""
] | Map-free relocalization technology is crucial for applications in autonomous
navigation and augmented reality, but relying on pre-built maps is often
impractical. It faces significant challenges due to limitations in matching
methods and the inherent lack of scale in monocular images. These issues lead
to substantial rotational and metric errors and even localization failures in
real-world scenarios. Large matching errors significantly impact the overall
relocalization process, affecting both rotational and translational accuracy.
Due to the inherent limitations of the camera itself, recovering the metric
scale from a single image is crucial, as this significantly impacts the
translation error. To address these challenges, we propose a map-free
relocalization method enhanced by instance knowledge and depth knowledge. By
leveraging instance-based matching information to improve global matching
results, our method significantly reduces the possibility of mismatching across
different objects. The robustness of instance knowledge across the scene helps
the feature point matching model focus on relevant regions and enhance matching
accuracy. Additionally, we use estimated metric depth from a single image to
reduce metric errors and improve scale recovery accuracy. By integrating
methods dedicated to mitigating large translational and rotational errors, our
approach demonstrates superior performance in map-free relocalization
techniques. | 17 pages,6 figures | cs.CV | [
"cs.CV",
"cs.AI"
] |
||
Avatar Visual Similarity for Social HCI: Increasing Self-Awareness | http://arxiv.org/abs/2408.13084v1 | http://arxiv.org/abs/2408.13084v1 | http://arxiv.org/pdf/2408.13084v1 | 2024-08-23 | 2024-08-23 | [
"Bernhard Hilpert",
"Claudio Alves da Silva",
"Leon Christidis",
"Chirag Bhuvaneshwara",
"Patrick Gebhard",
"Fabrizio Nunnari",
"Dimitra Tsovaltzi"
] | [
"",
"",
"",
"",
"",
"",
""
] | Self-awareness is a critical factor in social human-human interaction and,
hence, in social HCI interaction. Increasing self-awareness through mirrors or
video recordings is common in face-to-face trainings, since it influences
antecedents of self-awareness like explicit identification and implicit
affective identification (affinity). However, increasing self-awareness has
been scarcely examined in virtual trainings with virtual avatars, which allow
for adjusting the similarity, e.g. to avoid negative effects of
self-consciousness. Automatic visual similarity in avatars is an open issue
related to high costs. It is important to understand which features need to be
manipulated and which degree of similarity is necessary for self-awareness to
leverage the added value of using avatars for self-awareness. This article
examines the relationship between avatar visual similarity and increasing
self-awareness in virtual training environments. We define visual similarity
based on perceptually important facial features for human-human identification
and develop a theory-based methodology to systematically manipulate visual
similarity of virtual avatars and support self-awareness. Three personalized
versions of virtual avatars with varying degrees of visual similarity to
participants were created (weak, medium and strong facial features
manipulation). In a within-subject study (N=33), we tested effects of degree of
similarity on perceived similarity, explicit identification and implicit
affective identification (affinity). Results show significant differences
between the weak similarity manipulation, and both the strong manipulation and
the random avatar for all three antecedents of self-awareness. An increasing
degree of avatar visual similarity influences antecedents of self-awareness in
virtual environments. | cs.HC | [
"cs.HC",
"cs.AI"
] |
|||
Multivariate Time-Series Anomaly Detection based on Enhancing Graph
Attention Networks with Topological Analysis | http://arxiv.org/abs/2408.13082v1 | http://arxiv.org/abs/2408.13082v1 | http://arxiv.org/pdf/2408.13082v1 | 2024-08-23 | 2024-08-23 | [
"Zhe Liu",
"Xiang Huang",
"Jingyun Zhang",
"Zhifeng Hao",
"Li Sun",
"Hao Peng"
] | [
"",
"",
"",
"",
"",
""
] | Unsupervised anomaly detection in time series is essential in industrial
applications, as it significantly reduces the need for manual intervention.
Multivariate time series pose a complex challenge due to their feature and
temporal dimensions. Traditional methods use Graph Neural Networks (GNNs) or
Transformers to analyze spatial while RNNs to model temporal dependencies.
These methods focus narrowly on one dimension or engage in coarse-grained
feature extraction, which can be inadequate for large datasets characterized by
intricate relationships and dynamic changes. This paper introduces a novel
temporal model built on an enhanced Graph Attention Network (GAT) for
multivariate time series anomaly detection called TopoGDN. Our model analyzes
both time and feature dimensions from a fine-grained perspective. First, we
introduce a multi-scale temporal convolution module to extract detailed
temporal features. Additionally, we present an augmented GAT to manage complex
inter-feature dependencies, which incorporates graph topology into node
features across multiple scales, a versatile, plug-and-play enhancement that
significantly boosts the performance of GAT. Our experimental results confirm
that our approach surpasses the baseline models on four datasets, demonstrating
its potential for widespread application in fields requiring robust anomaly
detection. The code is available at https://github.com/ljj-cyber/TopoGDN. | 10 pages, 5 figures, to be published in CIKM 2024 | 10.1145/3627673.3679614 | cs.LG | [
"cs.LG",
"cs.AI"
] |
|
AEMLO: AutoEncoder-Guided Multi-Label Oversampling | http://arxiv.org/abs/2408.13078v1 | http://arxiv.org/abs/2408.13078v1 | http://arxiv.org/pdf/2408.13078v1 | 2024-08-23 | 2024-08-23 | [
"Ao Zhou",
"Bin Liu",
"Jin Wang",
"Kaiwei Sun",
"Kelin Liu"
] | [
"",
"",
"",
"",
""
] | Class imbalance significantly impacts the performance of multi-label
classifiers. Oversampling is one of the most popular approaches, as it augments
instances associated with less frequent labels to balance the class
distribution. Existing oversampling methods generate feature vectors of
synthetic samples through replication or linear interpolation and assign labels
through neighborhood information. Linear interpolation typically generates new
samples between existing data points, which may result in insufficient
diversity of synthesized samples and further lead to the overfitting issue.
Deep learning-based methods, such as AutoEncoders, have been proposed to
generate more diverse and complex synthetic samples, achieving excellent
performance on imbalanced binary or multi-class datasets. In this study, we
introduce AEMLO, an AutoEncoder-guided Oversampling technique specifically
designed for tackling imbalanced multi-label data. AEMLO is built upon two
fundamental components. The first is an encoder-decoder architecture that
enables the model to encode input data into a low-dimensional feature space,
learn its latent representations, and then reconstruct it back to its original
dimension, thus applying to the generation of new data. The second is an
objective function tailored to optimize the sampling task for multi-label
scenarios. We show that AEMLO outperforms the existing state-of-the-art methods
with extensive empirical studies. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
Hierarchical Spatio-Temporal State-Space Modeling for fMRI Analysis | http://arxiv.org/abs/2408.13074v1 | http://arxiv.org/abs/2408.13074v1 | http://arxiv.org/pdf/2408.13074v1 | 2024-08-23 | 2024-08-23 | [
"Yuxiang Wei",
"Anees Abrol",
"Reihaneh Hassanzadeh",
"Vince Calhoun"
] | [
"",
"",
"",
""
] | Recent advances in deep learning structured state space models, especially
the Mamba architecture, have demonstrated remarkable performance improvements
while maintaining linear complexity. In this study, we introduce functional
spatiotemporal Mamba (FST-Mamba), a Mamba-based model designed for discovering
neurological biomarkers using functional magnetic resonance imaging (fMRI). We
focus on dynamic functional network connectivity (dFNC) derived from fMRI and
propose a hierarchical spatiotemporal Mamba-based network that processes
spatial and temporal information separately using Mamba-based encoders.
Leveraging the topological uniqueness of the FNC matrix, we introduce a
component-wise varied-scale aggregation (CVA) mechanism to aggregate
connectivity across individual components within brain networks, enabling the
model to capture both inter-component and inter-network information. To better
handle the FNC data, we develop a new component-specific scanning order.
Additionally, we propose symmetric rotary position encoding (SymRope) to encode
the relative positions of each functional connection while considering the
symmetric nature of the FNC matrix. Experimental results demonstrate
significant improvements in the proposed FST-Mamba model on various brain-based
classification and regression tasks. Our work reveals the substantial potential
of attention-free sequence modeling in brain discovery. | cs.LG | [
"cs.LG",
"cs.AI"
] |