Papers: Difference between revisions
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'''[[Memorizing Transformers]]''' - https://arxiv.org/abs/2203.08913 - | '''[[Memorizing Transformers]]''' - https://arxiv.org/abs/2203.08913 - | ||
'''[[MobileViT]]''' - https://arxiv.org/abs/2110.02178 - Light-weight, General-purpose, and Mobile-friendly Vision Transformer | |||
'''[[OpenAI CLIP]]''' - https://arxiv.org/abs/2103.00020, https://openai.com/blog/clip/ - Learning Transferable Visual Models From Natural Language Supervision | '''[[OpenAI CLIP]]''' - https://arxiv.org/abs/2103.00020, https://openai.com/blog/clip/ - Learning Transferable Visual Models From Natural Language Supervision | ||
'''[[STaR]]''' - https://arxiv.org/abs/2203.14465 - Bootstrapping Reasoning With Reasoning | |||
'''[[Transformer-XL]]''' - https://arxiv.org/abs/1901.02860 - Attentive Language Models Beyond a Fixed-Length Context | '''[[Transformer-XL]]''' - https://arxiv.org/abs/1901.02860 - Attentive Language Models Beyond a Fixed-Length Context | ||
Revision as of 17:51, 5 February 2023
https://arxiv.org/abs/2301.13779 (FLAME: A small language model for spreadsheet formulas) - Small model specifically for spreadsheets by Miscrofot
Attention Is All You Need - https://arxiv.org/abs/1706.03762 - - influential paper that introduced Transformer
An Image is Worth 16x16 Words - https://arxiv.org/abs/2010.11929 - Transformers for Image Recognition at Scale - Vision Transformer (ViT)
Block-Recurrent Transformers - https://arxiv.org/abs/2203.07852
Language Models are Few-Shot Learners - https://arxiv.org/abs/2005.14165 - GPT
Memorizing Transformers - https://arxiv.org/abs/2203.08913 -
MobileViT - https://arxiv.org/abs/2110.02178 - Light-weight, General-purpose, and Mobile-friendly Vision Transformer
OpenAI CLIP - https://arxiv.org/abs/2103.00020, https://openai.com/blog/clip/ - Learning Transferable Visual Models From Natural Language Supervision
STaR - https://arxiv.org/abs/2203.14465 - Bootstrapping Reasoning With Reasoning
Transformer-XL - https://arxiv.org/abs/1901.02860 - Attentive Language Models Beyond a Fixed-Length Context