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] ||  || [[AlexNet]]
|[[ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)]] || 2012 || [https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf AlexNet Paper] ||  ||  || [[AlexNet]]
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|[[Efficient Estimation of Word Representations in Vector Space (Word2Vec)]] || 2013/01/16 || [[arxiv:1301.3781]] || [[NLP]] || [[Word2Vec]]
|[[Efficient Estimation of Word Representations in Vector Space (Word2Vec)]] || 2013/01/16 || [[arxiv:1301.3781]] || [[NLP]] ||  || [[Word2Vec]]
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|[[Playing Atari with Deep Reinforcement Learning (DQN)]] || 2013/12/19 || [[arxiv:1312.5602]] ||  || [[DQN]]
|[[Playing Atari with Deep Reinforcement Learning (DQN)]] || 2013/12/19 || [[arxiv:1312.5602]] ||  ||  || [[DQN]]
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|[[Generative Adversarial Networks (GAN)]] || 2014/06/10 || [[arxiv:1406.2661]] ||  || [[GAN]]
|[[Generative Adversarial Networks (GAN)]] || 2014/06/10 || [[arxiv:1406.2661]] ||  ||  || [[GAN]]
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|[[Very Deep Convolutional Networks for Large-Scale Image Recognition (VGGNet)]] || 2014/09/04 || [[arxiv:409.1556]] ||  || [[VGGNet]]
|[[Very Deep Convolutional Networks for Large-Scale Image Recognition (VGGNet)]] || 2014/09/04 || [[arxiv:409.1556]] ||  ||  || [[VGGNet]]
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|[[Sequence to Sequence Learning with Neural Networks (Seq2Seq)]] || 2014/09/10 || [[arxiv:1409.3215]] ||  || [[Seq2Seq]]
|[[Sequence to Sequence Learning with Neural Networks (Seq2Seq)]] || 2014/09/10 || [[arxiv:1409.3215]] ||  ||  || [[Seq2Seq]]
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|[[Adam: A Method for Stochastic Optimization)]] || 2014/12/22 || [[arxiv:1412.6980]] ||  || [[Adam]]
|[[Adam: A Method for Stochastic Optimization)]] || 2014/12/22 || [[arxiv:1412.6980]] ||  ||  || [[Adam]]
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|[[Deep Residual Learning for Image Recognition (ResNet)]] || 2015/12/10 || [[arxiv:409.1556]] ||  || [[ResNet]]
|[[Deep Residual Learning for Image Recognition (ResNet)]] || 2015/12/10 || [[arxiv:409.1556]] ||  ||  || [[ResNet]]
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|[[Going Deeper with Convolutions (GoogleNet)]] || 2015/12/10 || [[arxiv:409.1556]] ||  || [[GoogleNet]]
|[[Going Deeper with Convolutions (GoogleNet)]] || 2015/12/10 || [[arxiv:409.1556]] ||  ||  || [[GoogleNet]]
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|[[Asynchronous Methods for Deep Reinforcement Learning (A3C)]] || 2016/02/04 || [[arxiv:1602.01783]] ||  || [[A3C]]
|[[Asynchronous Methods for Deep Reinforcement Learning (A3C)]] || 2016/02/04 || [[arxiv:1602.01783]] ||  ||  || [[A3C]]
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|[[WaveNet: A Generative Model for Raw Audio]] || 2016/09/12 || [[arxiv:1609.03499]] || [[Audio]] || [[WaveNet]]
|[[WaveNet: A Generative Model for Raw Audio]] || 2016/09/12 || [[arxiv:1609.03499]] || [[Audio]] ||  || [[WaveNet]]
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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]] ||  || [[PPO]]
|[[Proximal Policy Optimization Algorithms (PPO)]] || 2017/07/20 || [[arxiv:1707.06347]] ||  ||  || [[PPO]]
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|[[Improving Language Understanding by Generative Pre-Training (GPT)]] || 2018 || [https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf paper source] || [[NLP]] || [[GPT]]
|[[Improving Language Understanding by Generative Pre-Training (GPT)]] || 2018 || [https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf paper source] || [[NLP]] ||  || [[GPT]]
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|[[Deep contextualized word representations (ELMo)]] || 2018/02/15 || [[arxiv:1802.05365]] || [[NLP]] || [[ELMo]]
|[[Deep contextualized word representations (ELMo)]] || 2018/02/15 || [[arxiv:1802.05365]] || [[NLP]] ||  || [[ELMo]]
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|[[GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding]] || 2018/04/20 || [[arxiv:1804.07461]] || [[NLP]] || [[GLUE]]
|[[GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding]] || 2018/04/20 || [[arxiv:1804.07461]] || [[NLP]] ||  || [[GLUE]]
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|[[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]] || 2018/10/11 || [[arxiv:1810.04805]] || [[NLP]] || [[BERT]]
|[[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]] || 2018/10/11 || [[arxiv:1810.04805]] || [[NLP]] ||  || [[BERT]]
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|[[Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context]] || 2019/01/09 || [[arxiv:1901.02860]] ||  || [[Transformer-XL]]
|[[Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context]] || 2019/01/09 || [[arxiv:1901.02860]] ||  ||  || [[Transformer-XL]]
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|[[Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model (MuZero)]] || 2019/11/19 || [[arxiv:1911.08265]] ||  || [[MuZero]]
|[[Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model (MuZero)]] || 2019/11/19 || [[arxiv:1911.08265]] ||  ||  || [[MuZero]]
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|[[Language Models are Few-Shot Learners (GPT-3)]] || 2020/05/28 || [[arxiv:2005.14165]] || [[NLP]] || [[GPT-3]]
|[[Language Models are Few-Shot Learners (GPT-3)]] || 2020/05/28 || [[arxiv:2005.14165]] || [[NLP]] ||  || [[GPT-3]]
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|[[An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT)]] || 2020/10/22 || [[arxiv:2010.11929]] ||  || [[Vision Transformer]] ([[ViT]])
|[[An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT)]] || 2020/10/22 || [[arxiv:2010.11929]] ||  ||  || [[Vision Transformer]] ([[ViT]])
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|[[Learning Transferable Visual Models From Natural Language Supervision (CLIP)]] || 2021/02/26 || [[arxiv:2103.00020]]<br>[https://openai.com/blog/clip/ OpenAI Blog] ||  ||  
|[[Learning Transferable Visual Models From Natural Language Supervision (CLIP)]] || 2021/02/26 || [[arxiv:2103.00020]]<br>[https://openai.com/blog/clip/ OpenAI Blog] ||  ||  ||  
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|[[MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer]] || 2021/10/05 || [[arxiv:2110.02178]] ||  || [[MobileViT]]
|[[MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer]] || 2021/10/05 || [[arxiv:2110.02178]] ||  ||  || [[MobileViT]]
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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: Bootstrapping Reasoning With Reasoning]] || 2022/03/28 || [[arxiv:2203.14465]] ||  || [[STaR]]
|[[STaR: Bootstrapping Reasoning With Reasoning]] || 2022/03/28 || [[arxiv:2203.14465]] ||  ||  || [[STaR]]
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|}

Revision as of 17:19, 6 February 2023

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Important Papers

Name Submission
Date 		Source Type Organization Note
ImageNet Classification with Deep Convolutional Neural Networks (AlexNet) 2012 AlexNet Paper AlexNet
Efficient Estimation of Word Representations in Vector Space (Word2Vec) 2013/01/16 arxiv:1301.3781 NLP Word2Vec
Playing Atari with Deep Reinforcement Learning (DQN) 2013/12/19 arxiv:1312.5602 DQN
Generative Adversarial Networks (GAN) 2014/06/10 arxiv:1406.2661 GAN
Very Deep Convolutional Networks for Large-Scale Image Recognition (VGGNet) 2014/09/04 arxiv:409.1556 VGGNet
Sequence to Sequence Learning with Neural Networks (Seq2Seq) 2014/09/10 arxiv:1409.3215 Seq2Seq
Adam: A Method for Stochastic Optimization) 2014/12/22 arxiv:1412.6980 Adam
Deep Residual Learning for Image Recognition (ResNet) 2015/12/10 arxiv:409.1556 ResNet
Going Deeper with Convolutions (GoogleNet) 2015/12/10 arxiv:409.1556 GoogleNet
Asynchronous Methods for Deep Reinforcement Learning (A3C) 2016/02/04 arxiv:1602.01783 A3C
WaveNet: A Generative Model for Raw Audio 2016/09/12 arxiv:1609.03499 Audio WaveNet
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 PPO
Improving Language Understanding by Generative Pre-Training (GPT) 2018 paper source NLP GPT
Deep contextualized word representations (ELMo) 2018/02/15 arxiv:1802.05365 NLP ELMo
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding 2018/04/20 arxiv:1804.07461 NLP GLUE
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding 2018/10/11 arxiv:1810.04805 NLP BERT
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context 2019/01/09 arxiv:1901.02860 Transformer-XL
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model (MuZero) 2019/11/19 arxiv:1911.08265 MuZero
Language Models are Few-Shot Learners (GPT-3) 2020/05/28 arxiv:2005.14165 NLP GPT-3
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT) 2020/10/22 arxiv:2010.11929 Vision Transformer (ViT)
Learning Transferable Visual Models From Natural Language Supervision (CLIP) 2021/02/26 arxiv:2103.00020
OpenAI Blog
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer 2021/10/05 arxiv:2110.02178 MobileViT
Block-Recurrent Transformers 2022/03/11 arxiv:2203.07852
Memorizing Transformers 2022/03/16 arxiv:2203.08913
STaR: Bootstrapping Reasoning With Reasoning 2022/03/28 arxiv:2203.14465 STaR

Other Papers

Name Submission
Date 		Source Type Organization Note
Dreamix: Video Diffusion Models are General Video Editors 2023/02/03 arxiv:2302.01329
blog post		 Google Dreamix
FLAME: A small language model for spreadsheet formulas 2023/01/31 arxiv:2301.13779 Microsoft FLAME
Muse: Text-To-Image Generation via Masked Generative Transformers 2023/01/02 arxiv:2301.00704
blog post		 Google Muse
Constitutional AI: Harmlessness from AI Feedback 2021/12/12 arxiv:2212.08073 Anthropic Constitutional AI, Claude
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