【2024年9月公開 Arxiv論文ランキング】2409.xxxxx

# AI論文解説 Youtubeチャンネル: AI時代の羅針盤 

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2024年9月頃に公開されたcsカテゴリの論文 (ID: 2409.xxxxx)を被引用数のデータを元にランキングしています。ランキングは随時更新します。
(2024年12月9日更新)
   
| 被引用数　　|タイトル|動画|
|------:|:--------|:------------|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.12191-black.svg)](https://arxiv.org/abs/2409.12191)162|<I>Qwen2-VL: Enhancing Vision-Language Model&#39;s Perception of the World at Any Resolution</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.12186-black.svg)](https://arxiv.org/abs/2409.12186)45|<I>Qwen2.5-Coder Technical Report</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.18869-black.svg)](https://arxiv.org/abs/2409.18869)33|<I>Emu3: Next-Token Prediction is All You Need</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/f218ad91-ac3e-a3bc-d80b-3c3411a5ee3c.png">](https://youtu.be/kr6LtY6YoIw)|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.17146-black.svg)](https://arxiv.org/abs/2409.17146)24|<I>Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/b7c4c9e9-c5dc-9065-97b9-d71a3b1ab66f.jpeg">](https://youtu.be/cuFEA7jIOt0)|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.04109-black.svg)](https://arxiv.org/abs/2409.04109)24|<I>Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/0b2ced22-80fb-c811-6f34-29fa33fb301b.png">](https://youtu.be/Es_zLXGAgNk)|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.14327-black.svg)](https://arxiv.org/abs/2409.14327)23|<I>Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.06765-black.svg)](https://arxiv.org/abs/2409.06765)23|<I>gsplat: An Open-Source Library for Gaussian Splatting</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.12183-black.svg)](https://arxiv.org/abs/2409.12183)22|<I>To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/d6b75955-f709-c653-f91f-e4cadaf43f81.png">](https://youtu.be/BOwvmgGOZmA)|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.12917-black.svg)](https://arxiv.org/abs/2409.12917)21|<I>Training Language Models to Self-Correct via Reinforcement Learning</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/d8d966e8-6a10-a6d2-933e-1c62eaaf04da.png">](https://youtu.be/MsAPGk1-FAg)|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.12122-black.svg)](https://arxiv.org/abs/2409.12122)20|<I>Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.02060-black.svg)](https://arxiv.org/abs/2409.02060)20|<I>OLMoE: Open Mixture-of-Experts Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.02048-black.svg)](https://arxiv.org/abs/2409.02048)20|<I>ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.17422-black.svg)](https://arxiv.org/abs/2409.17422)19|<I>Discovering the Gems in Early Layers: Accelerating Long-Context LLMs with 1000x Input Token Reduction</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/fc516cd8-7f51-03d7-68fb-3d089e77f885.png">](https://youtu.be/yt-tR43w6QQ)|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.06666-black.svg)](https://arxiv.org/abs/2409.06666)19|<I>LLaMA-Omni: Seamless Speech Interaction with Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.02095-black.svg)](https://arxiv.org/abs/2409.02095)19|<I>DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.18486-black.svg)](https://arxiv.org/abs/2409.18486)17|<I>Evaluation of OpenAI o1: Opportunities and Challenges of AGI</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.04429-black.svg)](https://arxiv.org/abs/2409.04429)17|<I>VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.02977-black.svg)](https://arxiv.org/abs/2409.02977)17|<I>Large Language Model-Based Agents for Software Engineering: A Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.01652-black.svg)](https://arxiv.org/abs/2409.01652)16|<I>ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.02813-black.svg)](https://arxiv.org/abs/2409.02813)14|<I>MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.18124-black.svg)](https://arxiv.org/abs/2409.18124)13|<I>Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.14972-black.svg)](https://arxiv.org/abs/2409.14972)13|<I>Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.13373-black.svg)](https://arxiv.org/abs/2409.13373)12|<I>LLMs Still Can&#39;t Plan; Can LRMs? A Preliminary Evaluation of OpenAI&#39;s o1 on PlanBench</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/80f99aa7-b13c-316a-cce1-abf477a65e1a.png">](https://youtu.be/swbPRiFxCbM)|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.11340-black.svg)](https://arxiv.org/abs/2409.11340)12|<I>OmniGen: Unified Image Generation</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/8652309e-31d1-65ce-3af3-5fec33b90047.png">](https://youtu.be/hiPrn31PaMY)|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.04410-black.svg)](https://arxiv.org/abs/2409.04410)12|<I>Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.18025-black.svg)](https://arxiv.org/abs/2409.18025)11|<I>An Adversarial Perspective on Machine Unlearning for AI Safety</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.10695-black.svg)](https://arxiv.org/abs/2409.10695)11|<I>Playground v3: Improving Text-to-Image Alignment with Deep-Fusion Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.02889-black.svg)](https://arxiv.org/abs/2409.02889)11|<I>LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via a Hybrid Architecture</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.02426-black.svg)](https://arxiv.org/abs/2409.02426)11|<I>Diffusion Models Learn Low-Dimensional Distributions via Subspace Clustering</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.02387-black.svg)](https://arxiv.org/abs/2409.02387)11|<I>Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.20566-black.svg)](https://arxiv.org/abs/2409.20566)10|<I>MM1.5: Methods, Analysis &amp; Insights from Multimodal LLM Fine-tuning</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/048b95b1-e449-5f08-eb7e-9cb627d5d454.png">](https://youtu.be/4di7xlh0ZGg)|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.17909-black.svg)](https://arxiv.org/abs/2409.17909)10|<I>Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessment</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.15314-black.svg)](https://arxiv.org/abs/2409.15314)10|<I>Reducing Bias in Deep Learning Optimization: The RSGDM Approach</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.13994-black.svg)](https://arxiv.org/abs/2409.13994)10|<I>Contrastive Learning for Knowledge-Based Question Generation in Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.12514-black.svg)](https://arxiv.org/abs/2409.12514)10|<I>TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.11402-black.svg)](https://arxiv.org/abs/2409.11402)10|<I>NVLM: Open Frontier-Class Multimodal LLMs</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/4b7a927c-4b18-b58b-0d06-b76a5d0c7233.png">](https://youtu.be/mnRuxdlpqtM)|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.05746-black.svg)](https://arxiv.org/abs/2409.05746)10|<I>LLMs Will Always Hallucinate, and We Need to Live With This</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/922c9114-529c-7e51-16b7-0b953a91d8bf.png">](https://youtu.be/T9CSYUHqvvs)|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.02392-black.svg)](https://arxiv.org/abs/2409.02392)10|<I>Building Math Agents with Multi-Turn Iterative Preference Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.18544-black.svg)](https://arxiv.org/abs/2409.18544)9|<I>Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.15576-black.svg)](https://arxiv.org/abs/2409.15576)9|<I>Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processing</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.09892-black.svg)](https://arxiv.org/abs/2409.09892)9|<I>Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.08395-black.svg)](https://arxiv.org/abs/2409.08395)9|<I>Graphical Structural Learning of rs-fMRI data in Heavy Smokers</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.07429-black.svg)](https://arxiv.org/abs/2409.07429)9|<I>Agent Workflow Memory</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/26e261c4-8266-842d-0f65-faac4f25ecdc.png">](https://youtu.be/HyTGVR2MSN8)|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.03930-black.svg)](https://arxiv.org/abs/2409.03930)9|<I>DRAL: Deep Reinforcement Adaptive Learning for Multi-UAVs Navigation in Unknown Indoor Environment</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.02634-black.svg)](https://arxiv.org/abs/2409.02634)9|<I>Loopy: Taming Audio-Driven Portrait Avatar with Long-Term Motion Dependency</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.01666-black.svg)](https://arxiv.org/abs/2409.01666)9|<I>In Defense of RAG in the Era of Long-Context Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.15332-black.svg)](https://arxiv.org/abs/2409.15332)8|<I>A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Images</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.14664-black.svg)](https://arxiv.org/abs/2409.14664)8|<I>Direct Judgement Preference Optimization</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.14000-black.svg)](https://arxiv.org/abs/2409.14000)8|<I>Graph Neural Network Framework for Sentiment Analysis Using Syntactic Feature</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.13346-black.svg)](https://arxiv.org/abs/2409.13346)8|<I>Imagine yourself: Tuning-Free Personalized Image Generation</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/36fffd23-5a11-6740-76f9-6cab0270db31.png">](https://youtu.be/OSmOrn9UrMQ)|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.10038-black.svg)](https://arxiv.org/abs/2409.10038)8|<I>On the Diagram of Thought</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.08264-black.svg)](https://arxiv.org/abs/2409.08264)8|<I>Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.08087-black.svg)](https://arxiv.org/abs/2409.08087)8|<I>Securing Large Language Models: Addressing Bias, Misinformation, and Prompt Attacks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.04977-black.svg)](https://arxiv.org/abs/2409.04977)8|<I>Enhancing Convolutional Neural Networks with Higher-Order Numerical Difference Methods</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.03752-black.svg)](https://arxiv.org/abs/2409.03752)8|<I>Attention Heads of Large Language Models: A Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.03733-black.svg)](https://arxiv.org/abs/2409.03733)8|<I>Planning In Natural Language Improves LLM Search For Code Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.01706-black.svg)](https://arxiv.org/abs/2409.01706)8|<I>Classically estimating observables of noiseless quantum circuits</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.01586-black.svg)](https://arxiv.org/abs/2409.01586)8|<I>Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.00899-black.svg)](https://arxiv.org/abs/2409.00899)8|<I>MarsCode Agent: AI-native Automated Bug Fixing</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.13868-black.svg)](https://arxiv.org/abs/2409.13868)7|<I>Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.09582-black.svg)](https://arxiv.org/abs/2409.09582)7|<I>NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.05865-black.svg)](https://arxiv.org/abs/2409.05865)7|<I>Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.05816-black.svg)](https://arxiv.org/abs/2409.05816)7|<I>Improving Pretraining Data Using Perplexity Correlations</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.04707-black.svg)](https://arxiv.org/abs/2409.04707)7|<I>Enhancing Deep Learning with Optimized Gradient Descent: Bridging Numerical Methods and Neural Network Training</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.03215-black.svg)](https://arxiv.org/abs/2409.03215)7|<I>xLAM: A Family of Large Action Models to Empower AI Agent Systems</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.02877-black.svg)](https://arxiv.org/abs/2409.02877)7|<I>Configurable Foundation Models: Building LLMs from a Modular Perspective</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.01704-black.svg)](https://arxiv.org/abs/2409.01704)7|<I>General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.01199-black.svg)](https://arxiv.org/abs/2409.01199)7|<I>OD-VAE: An Omni-dimensional Video Compressor for Improving Latent Video Diffusion Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.00608-black.svg)](https://arxiv.org/abs/2409.00608)7|<I>TinyAgent: Function Calling at the Edge</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.00088-black.svg)](https://arxiv.org/abs/2409.00088)7|<I>On-Device Language Models: A Comprehensive Review</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.16597-black.svg)](https://arxiv.org/abs/2409.16597)6|<I>EventHallusion: Diagnosing Event Hallucinations in Video LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.16211-black.svg)](https://arxiv.org/abs/2409.16211)6|<I>MaskBit: Embedding-free Image Generation via Bit Tokens</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.15402-black.svg)](https://arxiv.org/abs/2409.15402)6|<I>Uncovering Coordinated Cross-Platform Information Operations Threatening the Integrity of the 2024 U.S. Presidential Election Online Discussion</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.12347-black.svg)](https://arxiv.org/abs/2409.12347)6|<I>Axial Attention Transformer Networks: A New Frontier in Breast Cancer Detection</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.11564-black.svg)](https://arxiv.org/abs/2409.11564)6|<I>Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.07253-black.svg)](https://arxiv.org/abs/2409.07253)6|<I>Alignment of Diffusion Models: Fundamentals, Challenges, and Future</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.05976-black.svg)](https://arxiv.org/abs/2409.05976)6|<I>FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.05556-black.svg)](https://arxiv.org/abs/2409.05556)6|<I>SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.02908-black.svg)](https://arxiv.org/abs/2409.02908)6|<I>Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.00920-black.svg)](https://arxiv.org/abs/2409.00920)6|<I>ToolACE: Winning the Points of LLM Function Calling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.00588-black.svg)](https://arxiv.org/abs/2409.00588)6|<I>Diffusion Policy Policy Optimization</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.19603-black.svg)](https://arxiv.org/abs/2409.19603)5|<I>One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.18959-black.svg)](https://arxiv.org/abs/2409.18959)5|<I>$O(d/T)$ Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.18042-black.svg)](https://arxiv.org/abs/2409.18042)5|<I>EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.16280-black.svg)](https://arxiv.org/abs/2409.16280)5|<I>MonoFormer: One Transformer for Both Diffusion and Autoregression</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.16235-black.svg)](https://arxiv.org/abs/2409.16235)5|<I>EuroLLM: Multilingual Language Models for Europe</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.15647-black.svg)](https://arxiv.org/abs/2409.15647)5|<I>Looped Transformers for Length Generalization</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.15310-black.svg)](https://arxiv.org/abs/2409.15310)5|<I>Visual Prompting in Multimodal Large Language Models: A Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.15254-black.svg)](https://arxiv.org/abs/2409.15254)5|<I>Archon: An Architecture Search Framework for Inference-Time Techniques</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.14411-black.svg)](https://arxiv.org/abs/2409.14411)5|<I>Scaling Diffusion Policy in Transformer to 1 Billion Parameters for Robotic Manipulation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.14160-black.svg)](https://arxiv.org/abs/2409.14160)5|<I>Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.13598-black.svg)](https://arxiv.org/abs/2409.13598)5|<I>Prithvi WxC: Foundation Model for Weather and Climate</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2409.13503-black.svg)](https://arxiv.org/abs/2409.13503)5|<I>SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework</I>|not yet|
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※ 被引用数は更新日における NASA ADSのデータを参照しています
https://ui.adsabs.harvard.edu/