Papers: Difference between revisions

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|[[ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)]] || 2012 || [https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf AlexNet Paper] ||  
|[[ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)]] || 2012 || [https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf AlexNet Paper] ||  ||
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|[[Efficient Estimation of Word Representations in Vector Space (Word2Vec)]] || 2013/01/16 || [[arxiv:1301.3781]] || [[NLP]] ||  
|[[Efficient Estimation of Word Representations in Vector Space (Word2Vec)]] || 2013/01/16 || [[arxiv:1301.3781]] || [[NLP]] ||  
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|[[Playing Atari with Deep Reinforcement Learning (DQN)]] || 2013/12/19 || [[arxiv:1312.5602]] ||  
|[[Playing Atari with Deep Reinforcement Learning (DQN)]] || 2013/12/19 || [[arxiv:1312.5602]] ||  ||  
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|[[Very Deep Convolutional Networks for Large-Scale Image Recognition (VGGNet)]] || 2014/09/04 || [[arxiv:409.1556]] ||  
|[[Very Deep Convolutional Networks for Large-Scale Image Recognition (VGGNet)]] || 2014/09/04 || [[arxiv:409.1556]] ||  ||  
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|[[Deep Residual Learning for Image Recognition (ResNet)]] || 2015/12/10 || [[arxiv:409.1556]] ||  
|[[Deep Residual Learning for Image Recognition (ResNet)]] || 2015/12/10 || [[arxiv:409.1556]] ||  ||  
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|[[Going Deeper with Convolutions (GoogleNet)]] || 2015/12/10 || [[arxiv:409.1556]] ||  
|[[Going Deeper with Convolutions (GoogleNet)]] || 2015/12/10 || [[arxiv:409.1556]] ||  ||  
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|[[Asynchronous Methods for Deep Reinforcement Learning (A3C)]] || 2016/02/04 || [[arxiv:1602.01783]] ||  
|[[Asynchronous Methods for Deep Reinforcement Learning (A3C)]] || 2016/02/04 || [[arxiv:1602.01783]] ||  ||  
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|[[Attention Is All You Need (Transformer)]] || 2017/06/12 || [[arxiv:1706.03762]] || influential paper that introduced [[Transformer]]
|[[Attention Is All You Need (Transformer)]] || 2017/06/12 || [[arxiv:1706.03762]] ||  || influential paper that introduced [[Transformer]]
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|[[Proximal Policy Optimization Algorithms (PPO)]] || 2017/07/20 || [[arxiv:1707.06347]] ||  
|[[Proximal Policy Optimization Algorithms (PPO)]] || 2017/07/20 || [[arxiv:1707.06347]] ||  ||  
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|[[Transformer-XL]] || 2019/01/09 || [[arxiv:1901.02860]] || Attentive Language Models Beyond a Fixed-Length Context
|[[Transformer-XL]] || 2019/01/09 || [[arxiv:1901.02860]] ||  || Attentive Language Models Beyond a Fixed-Length Context
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|[[Language Models are Few-Shot Learners]] || 2020/05/28 || [[arxiv:2005.14165]] || [[GPT]]
|[[Language Models are Few-Shot Learners]] || 2020/05/28 || [[arxiv:2005.14165]] ||  || [[GPT]]
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|[[An Image is Worth 16x16 Words]] || 2020/10/22 || [[arxiv:2010.11929]] || Transformers for Image Recognition at Scale - [[Vision Transformer]] ([[ViT]])
|[[An Image is Worth 16x16 Words]] || 2020/10/22 || [[arxiv:2010.11929]] ||  || Transformers for Image Recognition at Scale - [[Vision Transformer]] ([[ViT]])
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|[[OpenAI CLIP]] || 2021/02/26 || [[arxiv:2103.00020]]<br>[https://openai.com/blog/clip/ OpenAI Blog] || Learning Transferable Visual Models From Natural Language Supervision
|[[OpenAI CLIP]] || 2021/02/26 || [[arxiv:2103.00020]]<br>[https://openai.com/blog/clip/ OpenAI Blog] ||  || Learning Transferable Visual Models From Natural Language Supervision
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|[[MobileViT]] || 2021/10/05 || [[arxiv:2110.02178]] || Light-weight, General-purpose, and Mobile-friendly Vision Transformer
|[[MobileViT]] || 2021/10/05 || [[arxiv:2110.02178]] ||  || Light-weight, General-purpose, and Mobile-friendly Vision Transformer
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|[[Block-Recurrent Transformers]] || 2022/03/11 || [[arxiv:2203.07852]] ||  
|[[Block-Recurrent Transformers]] || 2022/03/11 || [[arxiv:2203.07852]] ||  ||  
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|[[Memorizing Transformers]] || 2022/03/16 ||[[arxiv:2203.08913]] ||  
|[[Memorizing Transformers]] || 2022/03/16 ||[[arxiv:2203.08913]] ||  ||  
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|[[STaR]] || 2022/03/28 || [[arxiv:2203.14465]] || Bootstrapping Reasoning With Reasoning
|[[STaR]] || 2022/03/28 || [[arxiv:2203.14465]] ||  || Bootstrapping Reasoning With Reasoning
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Revision as of 02:25, 6 February 2023

Important

Name Submission
Date 		Source Type Note
ImageNet Classification with Deep Convolutional Neural Networks (AlexNet) 2012 AlexNet Paper
Efficient Estimation of Word Representations in Vector Space (Word2Vec) 2013/01/16 arxiv:1301.3781 NLP
Playing Atari with Deep Reinforcement Learning (DQN) 2013/12/19 arxiv:1312.5602
Very Deep Convolutional Networks for Large-Scale Image Recognition (VGGNet) 2014/09/04 arxiv:409.1556
Deep Residual Learning for Image Recognition (ResNet) 2015/12/10 arxiv:409.1556
Going Deeper with Convolutions (GoogleNet) 2015/12/10 arxiv:409.1556
Asynchronous Methods for Deep Reinforcement Learning (A3C) 2016/02/04 arxiv:1602.01783
Attention Is All You Need (Transformer) 2017/06/12 arxiv:1706.03762 influential paper that introduced Transformer
Proximal Policy Optimization Algorithms (PPO) 2017/07/20 arxiv:1707.06347
Transformer-XL 2019/01/09 arxiv:1901.02860 Attentive Language Models Beyond a Fixed-Length Context
Language Models are Few-Shot Learners 2020/05/28 arxiv:2005.14165 GPT
An Image is Worth 16x16 Words 2020/10/22 arxiv:2010.11929 Transformers for Image Recognition at Scale - Vision Transformer (ViT)
OpenAI CLIP 2021/02/26 arxiv:2103.00020
OpenAI Blog		 Learning Transferable Visual Models From Natural Language Supervision
MobileViT 2021/10/05 arxiv:2110.02178 Light-weight, General-purpose, and Mobile-friendly Vision Transformer
Block-Recurrent Transformers 2022/03/11 arxiv:2203.07852
Memorizing Transformers 2022/03/16 arxiv:2203.08913
STaR 2022/03/28 arxiv:2203.14465 Bootstrapping Reasoning With Reasoning

Others

https://arxiv.org/abs/2301.13779 (FLAME: A small language model for spreadsheet formulas) - Small model specifically for spreadsheets by Miscrofot

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