Base Models | Notion

Language Models

Attention? Attention! https://lilianweng.github.io/posts/2018-06-24-attention/ | Lilian Weng
The Transformer Family Version 2.0 https://lilianweng.github.io/posts/2023-01-27-the-transformer-family-v2/ | Lilian Weng
[NeurIPS’17] Attention Is All You Need https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf | Google Brain, Google Research, University of Toronto
[ArXiv 2023.08] Retentive Network: A Successor to Transformer for Large Language Models https://arxiv.org/pdf/2307.08621.pdf | Microsoft Research, Tsinghua University
[ArXiv 2023.11] System 2 Attention (is something you might need too) https://arxiv.org/abs/2311.11829 | Meta AI
[arXiv 2023.08] Link-Context Learning for Multimodal LLMs https://github.com/isekai-portal/Link-Context-Learning | SenseTime Research, Institute of Automation, CAS, Nanyang Technological University
[arXiv 2023.05] Learning without Forgetting for Vision-Language Models https://arxiv.org/abs/2305.19270
[NeurIPS’23] What Makes Good Examples for Visual In-Context Learning? https://github.com/ZhangYuanhan-AI/visual_prompt_retrieval
[NeurIPS’23] Large Language Models are Visual Reasoning Coordinators https://github.com/cliangyu/Cola

Contrastive Representation Learning https://lilianweng.github.io/posts/2021-05-31-contrastive/ | Lilian Weng
Google Research https://github.com/google-research/vision_transformer
- [ICLR’21 Oral] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale https://openreview.net/forum?id=YicbFdNTTy | Google Research, Google Brain
[ICCV’21 Best Paper Award] Swin Transformer: Hierarchical Vision Transformer using Shifted Windows https://arxiv.org/pdf/2103.14030.pdf | Microsoft Research
[NeurIPS’23] Rubik’s Cube: High-Order Channel Interactions with a Hierarchical Receptive Field https://openreview.net/pdf?id=hOOOvOMok5