Scheduled Commit

data/2411.12814.json

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+{"paper_url": "https://huggingface.co/papers/2411.12814", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Few Exemplar-Based General Medical Image Segmentation via Domain-Aware Selective Adaptation](https://huggingface.co/papers/2410.09254) (2024)\n* [Medical Image Segmentation with SAM-generated Annotations](https://huggingface.co/papers/2409.20253) (2024)\n* [MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation](https://huggingface.co/papers/2409.19483) (2024)\n* [Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision](https://huggingface.co/papers/2409.20293) (2024)\n* [Segment as You Wish -- Free-Form Language-Based Segmentation for Medical Images](https://huggingface.co/papers/2410.12831) (2024)\n* [CoSAM: Self-Correcting SAM for Domain Generalization in 2D Medical Image Segmentation](https://huggingface.co/papers/2411.10136) (2024)\n* [DB-SAM: Delving into High Quality Universal Medical Image Segmentation](https://huggingface.co/papers/2410.04172) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.13550.json

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+{"paper_url": "https://huggingface.co/papers/2411.13550", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [SAMPart3D: Segment Any Part in 3D Objects](https://huggingface.co/papers/2411.07184) (2024)\n* [Towards Open-Vocabulary Semantic Segmentation Without Semantic Labels](https://huggingface.co/papers/2409.19846) (2024)\n* [SA3DIP: Segment Any 3D Instance with Potential 3D Priors](https://huggingface.co/papers/2411.03819) (2024)\n* [BelHouse3D: A Benchmark Dataset for Assessing Occlusion Robustness in 3D Point Cloud Semantic Segmentation](https://huggingface.co/papers/2411.13251) (2024)\n* [Zero-Shot Scene Reconstruction from Single Images with Deep Prior Assembly](https://huggingface.co/papers/2410.15971) (2024)\n* [Open-RGBT: Open-vocabulary RGB-T Zero-shot Semantic Segmentation in Open-world Environments](https://huggingface.co/papers/2410.06626) (2024)\n* [PAVLM: Advancing Point Cloud based Affordance Understanding Via Vision-Language Model](https://huggingface.co/papers/2410.11564) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.14486.json

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+{"paper_url": "https://huggingface.co/papers/2411.14486", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Testing Uncertainty of Large Language Models for Physics Knowledge and Reasoning](https://huggingface.co/papers/2411.14465) (2024)\n* [HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics](https://huggingface.co/papers/2410.09988) (2024)\n* [Dynamic Intelligence Assessment: Benchmarking LLMs on the Road to AGI with a Focus on Model Confidence](https://huggingface.co/papers/2410.15490) (2024)\n* [Measuring short-form factuality in large language models](https://huggingface.co/papers/2411.04368) (2024)\n* [Improving Mathematical Reasoning Capabilities of Small Language Models via Feedback-Driven Distillation](https://huggingface.co/papers/2411.14698) (2024)\n* [Do LLMs estimate uncertainty well in instruction-following?](https://huggingface.co/papers/2410.14582) (2024)\n* [Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation](https://huggingface.co/papers/2411.00412) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.14522.json

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+{"paper_url": "https://huggingface.co/papers/2411.14522", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [A Survey of Medical Vision-and-Language Applications and Their Techniques](https://huggingface.co/papers/2411.12195) (2024)\n* [LLaVA-Ultra: Large Chinese Language and Vision Assistant for Ultrasound](https://huggingface.co/papers/2410.15074) (2024)\n* [SparrowVQE: Visual Question Explanation for Course Content Understanding](https://huggingface.co/papers/2411.07516) (2024)\n* [Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding](https://huggingface.co/papers/2410.23822) (2024)\n* [Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of Experts](https://huggingface.co/papers/2411.10669) (2024)\n* [LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language Interpretation](https://huggingface.co/papers/2411.09301) (2024)\n* [HumanVLM: Foundation for Human-Scene Vision-Language Model](https://huggingface.co/papers/2411.03034) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.14525.json

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+{"paper_url": "https://huggingface.co/papers/2411.14525", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [KA$^2$ER: Knowledge Adaptive Amalgamation of ExpeRts for Medical Images Segmentation](https://huggingface.co/papers/2410.21085) (2024)\n* [Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models](https://huggingface.co/papers/2411.14353) (2024)\n* [Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline](https://huggingface.co/papers/2411.12814) (2024)\n* [Selecting the Best Sequential Transfer Path for Medical Image Segmentation with Limited Labeled Data](https://huggingface.co/papers/2410.06892) (2024)\n* [Large-Scale 3D Medical Image Pre-training with Geometric Context Priors](https://huggingface.co/papers/2410.09890) (2024)\n* [MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation](https://huggingface.co/papers/2409.19483) (2024)\n* [Few Exemplar-Based General Medical Image Segmentation via Domain-Aware Selective Adaptation](https://huggingface.co/papers/2410.09254) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.15138.json

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+{"paper_url": "https://huggingface.co/papers/2411.15138", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [SSEditor: Controllable Mask-to-Scene Generation with Diffusion Model](https://huggingface.co/papers/2411.12290) (2024)\n* [MVLight: Relightable Text-to-3D Generation via Light-conditioned Multi-View Diffusion](https://huggingface.co/papers/2411.11475) (2024)\n* [OminiControl: Minimal and Universal Control for Diffusion Transformer](https://huggingface.co/papers/2411.15098) (2024)\n* [TEXGen: a Generative Diffusion Model for Mesh Textures](https://huggingface.co/papers/2411.14740) (2024)\n* [ARM: Appearance Reconstruction Model for Relightable 3D Generation](https://huggingface.co/papers/2411.10825) (2024)\n* [Any-to-3D Generation via Hybrid Diffusion Supervision](https://huggingface.co/papers/2411.14715) (2024)\n* [GANESH: Generalizable NeRF for Lensless Imaging](https://huggingface.co/papers/2411.04810) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.15221.json

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+{"paper_url": "https://huggingface.co/papers/2411.15221", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Polymetis:Large Language Modeling for Multiple Material Domains](https://huggingface.co/papers/2411.08728) (2024)\n* [Tooling or Not Tooling? The Impact of Tools on Language Agents for Chemistry Problem Solving](https://huggingface.co/papers/2411.07228) (2024)\n* [Challenges in Guardrailing Large Language Models for Science](https://huggingface.co/papers/2411.08181) (2024)\n* [ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery](https://huggingface.co/papers/2410.05080) (2024)\n* [A Layered Architecture for Developing and Enhancing Capabilities in Large Language Model-based Software Systems](https://huggingface.co/papers/2411.12357) (2024)\n* [Towards unearthing neglected climate innovations from scientific literature using Large Language Models](https://huggingface.co/papers/2411.10055) (2024)\n* [LLM4DS: Evaluating Large Language Models for Data Science Code Generation](https://huggingface.co/papers/2411.11908) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.15466.json

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+{"paper_url": "https://huggingface.co/papers/2411.15466", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [RelationBooth: Towards Relation-Aware Customized Object Generation](https://huggingface.co/papers/2410.23280) (2024)\n* [MagicEraser: Erasing Any Objects via Semantics-Aware Control](https://huggingface.co/papers/2410.10207) (2024)\n* [3DIS: Depth-Driven Decoupled Instance Synthesis for Text-to-Image Generation](https://huggingface.co/papers/2410.12669) (2024)\n* [TextCtrl: Diffusion-based Scene Text Editing with Prior Guidance Control](https://huggingface.co/papers/2410.10133) (2024)\n* [DisEnvisioner: Disentangled and Enriched Visual Prompt for Customized Image Generation](https://huggingface.co/papers/2410.02067) (2024)\n* [Boundary Attention Constrained Zero-Shot Layout-To-Image Generation](https://huggingface.co/papers/2411.10495) (2024)\n* [Region-Aware Text-to-Image Generation via Hard Binding and Soft Refinement](https://huggingface.co/papers/2411.06558) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.15611.json

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+{"paper_url": "https://huggingface.co/papers/2411.15611", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Cross-Modal Consistency in Multimodal Large Language Models](https://huggingface.co/papers/2411.09273) (2024)\n* [In the Era of Prompt Learning with Vision-Language Models](https://huggingface.co/papers/2411.04892) (2024)\n* [Improving Multi-modal Large Language Model through Boosting Vision Capabilities](https://huggingface.co/papers/2410.13733) (2024)\n* [Efficient Transfer Learning for Video-language Foundation Models](https://huggingface.co/papers/2411.11223) (2024)\n* [TransAgent: Transfer Vision-Language Foundation Models with Heterogeneous Agent Collaboration](https://huggingface.co/papers/2410.12183) (2024)\n* [Multimodal LLM Enhanced Cross-lingual Cross-modal Retrieval](https://huggingface.co/papers/2409.19961) (2024)\n* [LLM2CLIP: Powerful Language Model Unlocks Richer Visual Representation](https://huggingface.co/papers/2411.04997) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.15671.json

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+{"paper_url": "https://huggingface.co/papers/2411.15671", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Learning Graph Quantized Tokenizers for Transformers](https://huggingface.co/papers/2410.13798) (2024)\n* [Homomorphism Counts as Structural Encodings for Graph Learning](https://huggingface.co/papers/2410.18676) (2024)\n* [Rethinking Graph Transformer Architecture Design for Node Classification](https://huggingface.co/papers/2410.11189) (2024)\n* [G-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning](https://huggingface.co/papers/2411.01532) (2024)\n* [A Hierarchical Language Model For Interpretable Graph Reasoning](https://huggingface.co/papers/2410.22372) (2024)\n* [Scalable Message Passing Neural Networks: No Need for Attention in Large Graph Representation Learning](https://huggingface.co/papers/2411.00835) (2024)\n* [Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention](https://huggingface.co/papers/2410.06746) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.15862.json

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+{"paper_url": "https://huggingface.co/papers/2411.15862", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Understanding Chain-of-Thought in LLMs through Information Theory](https://huggingface.co/papers/2411.11984) (2024)\n* [Gap-Filling Prompting Enhances Code-Assisted Mathematical Reasoning](https://huggingface.co/papers/2411.05407) (2024)\n* [A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration](https://huggingface.co/papers/2410.16540) (2024)\n* [Let's Be Self-generated via Step by Step: A Curriculum Learning Approach to Automated Reasoning with Large Language Models](https://huggingface.co/papers/2410.21728) (2024)\n* [Language Models are Hidden Reasoners: Unlocking Latent Reasoning Capabilities via Self-Rewarding](https://huggingface.co/papers/2411.04282) (2024)\n* [Patience Is The Key to Large Language Model Reasoning](https://huggingface.co/papers/2411.13082) (2024)\n* [AtomThink: A Slow Thinking Framework for Multimodal Mathematical Reasoning](https://huggingface.co/papers/2411.11930) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.16034.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2411.16034", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [PerSRV: Personalized Sticker Retrieval with Vision-Language Model](https://huggingface.co/papers/2410.21801) (2024)\n* [Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with Large Language Models for Multi-Behavior Recommendations](https://huggingface.co/papers/2410.12228) (2024)\n* [Sequential LLM Framework for Fashion Recommendation](https://huggingface.co/papers/2410.11327) (2024)\n* [STAR: A Simple Training-free Approach for Recommendations using Large Language Models](https://huggingface.co/papers/2410.16458) (2024)\n* [GenUP: Generative User Profilers as In-Context Learners for Next POI Recommender Systems](https://huggingface.co/papers/2410.20643) (2024)\n* [ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning](https://huggingface.co/papers/2410.23180) (2024)\n* [MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs](https://huggingface.co/papers/2411.02571) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.16035.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2411.16035", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Scaling Laws for Predicting Downstream Performance in LLMs](https://huggingface.co/papers/2410.08527) (2024)\n* [Loss-to-Loss Prediction: Scaling Laws for All Datasets](https://huggingface.co/papers/2411.12925) (2024)\n* [A Hitchhiker's Guide to Scaling Law Estimation](https://huggingface.co/papers/2410.11840) (2024)\n* [Bayesian scaling laws for in-context learning](https://huggingface.co/papers/2410.16531) (2024)\n* [Scaling Optimal LR Across Token Horizons](https://huggingface.co/papers/2409.19913) (2024)\n* [Scaling Laws for Precision](https://huggingface.co/papers/2411.04330) (2024)\n* [Sparse Upcycling: Inference Inefficient Finetuning](https://huggingface.co/papers/2411.08968) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.16085.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2411.16085", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [A second-order-like optimizer with adaptive gradient scaling for deep learning](https://huggingface.co/papers/2410.05871) (2024)\n* [Natural GaLore: Accelerating GaLore for memory-efficient LLM Training and Fine-tuning](https://huggingface.co/papers/2410.16029) (2024)\n* [Adam Exploits \u2113\u221e-geometry of Loss Landscape via Coordinate-wise Adaptivity](https://huggingface.co/papers/2410.08198) (2024)\n* [ADOPT: Modified Adam Can Converge with Any $\\beta_2$ with the Optimal Rate](https://huggingface.co/papers/2411.02853) (2024)\n* [FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training](https://huggingface.co/papers/2411.07837) (2024)\n* [MomentumSMoE: Integrating Momentum into Sparse Mixture of Experts](https://huggingface.co/papers/2410.14574) (2024)\n* [Old Optimizer, New Norm: An Anthology](https://huggingface.co/papers/2409.20325) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.16205.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2411.16205", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [CartesianMoE: Boosting Knowledge Sharing among Experts via Cartesian Product Routing in Mixture-of-Experts](https://huggingface.co/papers/2410.16077) (2024)\n* [Upcycling Large Language Models into Mixture of Experts](https://huggingface.co/papers/2410.07524) (2024)\n* [Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of Experts](https://huggingface.co/papers/2411.10669) (2024)\n* [MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts](https://huggingface.co/papers/2410.07348) (2024)\n* [ViMoE: An Empirical Study of Designing Vision Mixture-of-Experts](https://huggingface.co/papers/2410.15732) (2024)\n* [On Expert Estimation in Hierarchical Mixture of Experts: Beyond Softmax Gating Functions](https://huggingface.co/papers/2410.02935) (2024)\n* [MC-MoE: Mixture Compressor for Mixture-of-Experts LLMs Gains More](https://huggingface.co/papers/2410.06270) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.16318.json

@@ -0,0 +1 @@
+{"paper_url": "https://huggingface.co/papers/2411.16318", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [A Simple Approach to Unifying Diffusion-based Conditional Generation](https://huggingface.co/papers/2410.11439) (2024)\n* [FlexGen: Flexible Multi-View Generation from Text and Image Inputs](https://huggingface.co/papers/2410.10745) (2024)\n* [LaVin-DiT: Large Vision Diffusion Transformer](https://huggingface.co/papers/2411.11505) (2024)\n* [Depth Any Video with Scalable Synthetic Data](https://huggingface.co/papers/2410.10815) (2024)\n* [Adapting Diffusion Models for Improved Prompt Compliance and Controllable Image Synthesis](https://huggingface.co/papers/2410.21638) (2024)\n* [From Text to Pose to Image: Improving Diffusion Model Control and Quality](https://huggingface.co/papers/2411.12872) (2024)\n* [OmniBooth: Learning Latent Control for Image Synthesis with Multi-modal Instruction](https://huggingface.co/papers/2410.04932) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.16341.json

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+{"paper_url": "https://huggingface.co/papers/2411.16341", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Advancing Cloud Computing Capabilities on gem5 by Implementing the RISC-V Hypervisor Extension](https://huggingface.co/papers/2411.12444) (2024)\n* [Web-Based Simulator of Superscalar RISC-V Processors](https://huggingface.co/papers/2411.07721) (2024)\n* [Green My LLM: Studying the key factors affecting the energy consumption of code assistants](https://huggingface.co/papers/2411.11892) (2024)\n* [Can Large-Language Models Help us Better Understand and Teach the Development of Energy-Efficient Software?](https://huggingface.co/papers/2411.08912) (2024)\n* [Large Language Models for Energy-Efficient Code: Emerging Results and Future Directions](https://huggingface.co/papers/2410.09241) (2024)\n* [A Review on Edge Large Language Models: Design, Execution, and Applications](https://huggingface.co/papers/2410.11845) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.16443.json

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+{"paper_url": "https://huggingface.co/papers/2411.16443", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views](https://huggingface.co/papers/2411.04924) (2024)\n* [3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with View-consistent 2D Diffusion Priors](https://huggingface.co/papers/2410.16266) (2024)\n* [GaussianAnything: Interactive Point Cloud Latent Diffusion for 3D Generation](https://huggingface.co/papers/2411.08033) (2024)\n* [MvDrag3D: Drag-based Creative 3D Editing via Multi-view Generation-Reconstruction Priors](https://huggingface.co/papers/2410.16272) (2024)\n* [GS-VTON: Controllable 3D Virtual Try-on with Gaussian Splatting](https://huggingface.co/papers/2410.05259) (2024)\n* [Dual Encoder GAN Inversion for High-Fidelity 3D Head Reconstruction from Single Images](https://huggingface.co/papers/2409.20530) (2024)\n* [VistaDream: Sampling multiview consistent images for single-view scene reconstruction](https://huggingface.co/papers/2410.16892) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2411.16489.json

@@ -0,0 +1 @@
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data/2411.16508.json

@@ -0,0 +1 @@
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