【2024年8月公開 Arxiv論文ランキング】2408.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8月頃に公開されたcsカテゴリの論文 (ID: 2408.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-2408.00118-black.svg)](https://arxiv.org/abs/2408.00118)270|<I>Gemma 2: Improving Open Language Models at a Practical Size</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/1fdb1eb8-4fff-7759-c7bb-990cd71acec3.jpeg">](https://youtu.be/O_Pa2a2SmYY)|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.00714-black.svg)](https://arxiv.org/abs/2408.00714)200|<I>SAM 2: Segment Anything in Images and Videos</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/c33a9799-0d64-b8a3-ed5e-5bd388f20991.png">](https://youtu.be/EiwRzSMCNHI)|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.03326-black.svg)](https://arxiv.org/abs/2408.03326)129|<I>LLaVA-OneVision: Easy Visual Task Transfer</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.01800-black.svg)](https://arxiv.org/abs/2408.01800)107|<I>MiniCPM-V: A GPT-4V Level MLLM on Your Phone</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/6d90606c-7113-0342-fa14-f8f807876c7a.png">](https://youtu.be/HXzFaYuieg4)|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.06072-black.svg)](https://arxiv.org/abs/2408.06072)101|<I>CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.03314-black.svg)](https://arxiv.org/abs/2408.03314)84|<I>Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/89c875ac-e2c7-777b-1535-4971e930f765.png">](https://youtu.be/sDfnFyWtTUE)|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.06292-black.svg)](https://arxiv.org/abs/2408.06292)57|<I>The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/f2dfd68b-409c-fde5-52fb-d18a105c92ad.png">](https://youtu.be/9Oa1ZY833jc)|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.11039-black.svg)](https://arxiv.org/abs/2408.11039)50|<I>Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.12528-black.svg)](https://arxiv.org/abs/2408.12528)48|<I>Show-o: One Single Transformer to Unify Multimodal Understanding and Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.16500-black.svg)](https://arxiv.org/abs/2408.16500)32|<I>CogVLM2: Visual Language Models for Image and Video Understanding</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.05147-black.svg)](https://arxiv.org/abs/2408.05147)31|<I>Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.10205-black.svg)](https://arxiv.org/abs/2408.10205)28|<I>KAN 2.0: Kolmogorov-Arnold Networks Meet Science</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.05211-black.svg)](https://arxiv.org/abs/2408.05211)28|<I>VITA: Towards Open-Source Interactive Omni Multimodal LLM</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/c3e4b718-a809-e7dd-c05d-c3ce0f4bfb97.png">](https://youtu.be/_TiNle0IgKc)|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.15240-black.svg)](https://arxiv.org/abs/2408.15240)26|<I>Generative Verifiers: Reward Modeling as Next-Token Prediction</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.08872-black.svg)](https://arxiv.org/abs/2408.08872)26|<I>xGen-MM (BLIP-3): A Family of Open Large Multimodal Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.02666-black.svg)](https://arxiv.org/abs/2408.02666)25|<I>Self-Taught Evaluators</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.07666-black.svg)](https://arxiv.org/abs/2408.07666)24|<I>Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/4c66d13c-817c-fa14-b4ea-f578dcaf3be6.png">](https://youtu.be/1dhcgHg0C84)|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.04840-black.svg)](https://arxiv.org/abs/2408.04840)24|<I>mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.00874-black.svg)](https://arxiv.org/abs/2408.00874)23|<I>Medical SAM 2: Segment medical images as video via Segment Anything Model 2</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/97ddc4d9-ea8d-5859-b377-07aa7885a755.png">](https://youtu.be/EiwRzSMCNHI)|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.06070-black.svg)](https://arxiv.org/abs/2408.06070)22|<I>ControlNeXt: Powerful and Efficient Control for Image and Video Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.04585-black.svg)](https://arxiv.org/abs/2408.04585)21|<I>Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.07199-black.svg)](https://arxiv.org/abs/2408.07199)20|<I>Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/5068344e-f468-3113-e9a1-c5ca7d1e3536.png">](https://youtu.be/p1DF36eFfCw)|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.00761-black.svg)](https://arxiv.org/abs/2408.00761)20|<I>Tamper-Resistant Safeguards for Open-Weight LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.16068-black.svg)](https://arxiv.org/abs/2408.16068)19|<I>Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.12637-black.svg)](https://arxiv.org/abs/2408.12637)18|<I>Building and better understanding vision-language models: insights and future directions</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.07009-black.svg)](https://arxiv.org/abs/2408.07009)18|<I>Imagen 3</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/688b122a-a835-7c8f-fba7-80e57bb366ae.png">](https://youtu.be/kYSsYjrrCBY)|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.02657-black.svg)](https://arxiv.org/abs/2408.02657)18|<I>Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative Pretraining</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.11839-black.svg)](https://arxiv.org/abs/2408.11839)17|<I>Adaptive Friction in Deep Learning: Enhancing Optimizers with Sigmoid and Tanh Function</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.02442-black.svg)](https://arxiv.org/abs/2408.02442)17|<I>Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.02320-black.svg)](https://arxiv.org/abs/2408.02320)16|<I>A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.08435-black.svg)](https://arxiv.org/abs/2408.08435)15|<I>Automated Design of Agentic Systems</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.06195-black.svg)](https://arxiv.org/abs/2408.06195)15|<I>Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/d9367533-d157-3c06-6031-2d1886cfdc96.png">](https://youtu.be/hSTkwWI543A)|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.00724-black.svg)](https://arxiv.org/abs/2408.00724)15|<I>Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.16737-black.svg)](https://arxiv.org/abs/2408.16737)14|<I>Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/9ea254cc-aaeb-2647-76e1-055bcc17efca.png">](https://youtu.be/gPmNoCXIDVs)|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.16725-black.svg)](https://arxiv.org/abs/2408.16725)14|<I>Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.16633-black.svg)](https://arxiv.org/abs/2408.16633)14|<I>Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.14837-black.svg)](https://arxiv.org/abs/2408.14837)14|<I>Diffusion Models Are Real-Time Game Engines</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/530bc6af-321c-6cf0-813d-aba4140149ad.png">](https://youtu.be/z4bv2yius3Y)|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.10188-black.svg)](https://arxiv.org/abs/2408.10188)14|<I>LongVILA: Scaling Long-Context Visual Language Models for Long Videos</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.08152-black.svg)](https://arxiv.org/abs/2408.08152)14|<I>DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.02479-black.svg)](https://arxiv.org/abs/2408.02479)14|<I>From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.15221-black.svg)](https://arxiv.org/abs/2408.15221)13|<I>LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/6c1d2d64-339d-6bd2-1854-07be4945c7c1.png">](https://youtu.be/zegZ_Nr5dAo)|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.13233-black.svg)](https://arxiv.org/abs/2408.13233)13|<I>Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.12588-black.svg)](https://arxiv.org/abs/2408.12588)13|<I>Real-Time Video Generation with Pyramid Attention Broadcast</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.08288-black.svg)](https://arxiv.org/abs/2408.08288)13|<I>A universal neutral-atom quantum computer with individual optical addressing and non-destructive readout</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.04268-black.svg)](https://arxiv.org/abs/2408.04268)13|<I>Evaluating Modern Approaches in 3D Scene Reconstruction: NeRF vs Gaussian-Based Methods</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.04216-black.svg)](https://arxiv.org/abs/2408.04216)13|<I>Attention Mechanism and Context Modeling System for Text Mining Machine Translation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.03497-black.svg)](https://arxiv.org/abs/2408.03497)13|<I>Advanced User Credit Risk Prediction Model using LightGBM, XGBoost and Tabnet with SMOTEENN</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.15998-black.svg)](https://arxiv.org/abs/2408.15998)12|<I>Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/02da7c44-1e94-e530-ad4b-cbb874891049.png">](https://youtu.be/ts_q-ql3F3M)|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.08632-black.svg)](https://arxiv.org/abs/2408.08632)12|<I>A Survey on Benchmarks of Multimodal Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.07055-black.svg)](https://arxiv.org/abs/2408.07055)12|<I>LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.06634-black.svg)](https://arxiv.org/abs/2408.06634)12|<I>Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/b568bdda-3921-a36c-dcd1-9071b9abf832.png">](https://youtu.be/9gDM8PUnbww)|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.04940-black.svg)](https://arxiv.org/abs/2408.04940)12|<I>Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.04138-black.svg)](https://arxiv.org/abs/2408.04138)12|<I>Enhancing Healthcare through Large Language Models: A Study on Medical Question Answering</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.03361-black.svg)](https://arxiv.org/abs/2408.03361)12|<I>GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.03322-black.svg)](https://arxiv.org/abs/2408.03322)12|<I>Segment Anything in Medical Images and Videos: Benchmark and Deployment</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.02900-black.svg)](https://arxiv.org/abs/2408.02900)12|<I>MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.16760-black.svg)](https://arxiv.org/abs/2408.16760)11|<I>OmniRe: Omni Urban Scene Reconstruction</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.16532-black.svg)](https://arxiv.org/abs/2408.16532)11|<I>WavTokenizer: an Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modeling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.15542-black.svg)](https://arxiv.org/abs/2408.15542)11|<I>Kangaroo: A Powerful Video-Language Model Supporting Long-context Video Input</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.12570-black.svg)](https://arxiv.org/abs/2408.12570)11|<I>Jamba-1.5: Hybrid Transformer-Mamba Models at Scale</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.12151-black.svg)](https://arxiv.org/abs/2408.12151)11|<I>A Tighter Complexity Analysis of SparseGPT</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.11791-black.svg)](https://arxiv.org/abs/2408.11791)11|<I>Critique-out-Loud Reward Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.08921-black.svg)](https://arxiv.org/abs/2408.08921)11|<I>Graph Retrieval-Augmented Generation: A Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.07084-black.svg)](https://arxiv.org/abs/2408.07084)11|<I>Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.06327-black.svg)](https://arxiv.org/abs/2408.06327)11|<I>VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.03539-black.svg)](https://arxiv.org/abs/2408.03539)11|<I>Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.02622-black.svg)](https://arxiv.org/abs/2408.02622)11|<I>Language Model Can Listen While Speaking</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.15323-black.svg)](https://arxiv.org/abs/2408.15323)10|<I>Review: Quantum Metrology and Sensing with Many-Body Systems</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.13257-black.svg)](https://arxiv.org/abs/2408.13257)10|<I>MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.08926-black.svg)](https://arxiv.org/abs/2408.08926)10|<I>Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.06357-black.svg)](https://arxiv.org/abs/2408.06357)10|<I>Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Description</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.04682-black.svg)](https://arxiv.org/abs/2408.04682)10|<I>ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.03568-black.svg)](https://arxiv.org/abs/2408.03568)10|<I>A comparative study of generative adversarial networks for image recognition algorithms based on deep learning and traditional methods</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.03084-black.svg)](https://arxiv.org/abs/2408.03084)10|<I>Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.01129-black.svg)](https://arxiv.org/abs/2408.01129)10|<I>A Survey of Mamba</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.00741-black.svg)](https://arxiv.org/abs/2408.00741)10|<I>DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.15257-black.svg)](https://arxiv.org/abs/2408.15257)9|<I>Text classification optimization algorithm based on graph neural network</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.14033-black.svg)](https://arxiv.org/abs/2408.14033)9|<I>MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.12076-black.svg)](https://arxiv.org/abs/2408.12076)9|<I>ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLM</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.10130-black.svg)](https://arxiv.org/abs/2408.10130)9|<I>Rhyme-aware Chinese lyric generator based on GPT</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.09600-black.svg)](https://arxiv.org/abs/2408.09600)9|<I>Antidote: Post-fine-tuning Safety Alignment for Large Language Models against Harmful Fine-tuning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.07057-black.svg)](https://arxiv.org/abs/2408.07057)9|<I>A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.05178-black.svg)](https://arxiv.org/abs/2408.05178)9|<I>ECG-FM: An Open Electrocardiogram Foundation Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.02074-black.svg)](https://arxiv.org/abs/2408.02074)9|<I>Applying Conditional Generative Adversarial Networks for Imaging Diagnosis</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.00415-black.svg)](https://arxiv.org/abs/2408.00415)9|<I>DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.16767-black.svg)](https://arxiv.org/abs/2408.16767)8|<I>ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.12739-black.svg)](https://arxiv.org/abs/2408.12739)8|<I>Quantum Convolutional Neural Networks are (Effectively) Classically Simulable</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.12605-black.svg)](https://arxiv.org/abs/2408.12605)8|<I>Convolutional Neural Networks for Predictive Modeling of Lung Disease</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2408.11812-black.svg)](https://arxiv.org/abs/2408.11812)8|<I>Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation</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/