2024-11-18

Models

• nvidia/Hymba-1.5B-Base · Hugging Face
• Cosmos Tokenizer: A suite of image and video neural tokenizers #star
  • GitHub - NVIDIA/Cosmos-Tokenizer: A suite of image and video neural tokenizers
• Nexusflow/Athene-V2-Agent · Hugging Face

Papers

• [2411.07975] JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation
• [2410.08020] Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs
• [2411.10440] LLaVA-o1: Let Vision Language Models Reason Step-by-Step
• [2411.10433] M-VAR: Decoupled Scale-wise Autoregressive Modeling for High-Quality Image Generation
• [2411.13676] Hymba: A Hybrid-head Architecture for Small Language Models
• [2411.14402] Multimodal Autoregressive Pre-training of Large Vision Encoders #star
• [2411.14347] DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding #star
• [2411.12155] Reinforcement Learning with Action Sequence for Data-Efficient Robot Learning
• [2411.14429] Revisiting the Integration of Convolution and Attention for Vision Backbone

Code

• GitHub - apple/ml-aim: This repository provides the code and model checkpoints for AIMv1 and AIMv2 research projects.
• GitHub - Lightricks/LTX-Video: Official repository for LTX-Video
• GitHub - rayleizhu/GLMix: [NeurIPS 2024] official code release for our paper “Revisiting the Integration of Convolution and Attention for Vision Backbone”.

Articles

• You could have designed state of the art positional encoding
• Extending the Context Length to 1M Tokens! | Qwen

Videos

• Tim Dettmers on Open-source AI, LMs, SWE Bench, Agents, Quantization, & Optimization - YouTube
• Speculations on Test-Time Scaling (o1) - YouTube
  • Speculations on Test-Time Scaling | Richard M. Karp Distinguished Lecture - YouTube
• Retrieval augmented generation; Extractive summarization - YouTube
• Learning at test time in LLMs - YouTube
• QA: Retrieval & Answer extraction - YouTube
• Flash Attention derived and coded from first principles with Triton (Python) - YouTube
• Guest Lecture 1: Or Patashnik - The Power of Attention Layers (KAIST CS492D, Fall 2024) - YouTube

Other

• CS 886: Recent Advances on Foundation Models
• Stanford Graph Learning Workshop 2024 | Stanford Engineering Data Science Applications

Tweets

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