2024-11-04

Models

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Papers

• [2410.23262] EMMA: End-to-End Multimodal Model for Autonomous Driving
• [2411.03313] Classification Done Right for Vision-Language Pre-Training
• [2406.06484] Parallelizing Linear Transformers with the Delta Rule over Sequence Length
• [2411.02959] HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems
• [2411.04965] BitNet a4.8: 4-bit Activations for 1-bit LLMs
• [2411.04996] Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models
• [2411.04905] OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models
• [2410.17897] Value Residual Learning For Alleviating Attention Concentration In Transformers
• [2410.21228] LoRA vs Full Fine-tuning: An Illusion of Equivalence
• [2407.10964] No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations
• [2405.17604] LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters
• [2411.02853] ADOPT: Modified Adam Can Converge with Any $\beta \_2$ with the Optimal Rate

Code

• GitHub - run-ai/runai-model-streamer

Articles

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Videos

• YouTube
• Stanford Graph Learning Workshop 2024 - YouTube
• https://www.youtube.com/watch?v=0Yi3yUjB-3M&list=PPSV

Other

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Tweets