Papers: Difference between revisions

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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]] || [[Natural Language Processing]] ||  || [[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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|[[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]] || || [[Google]] || Influential paper that introduced [[Transformer]]
|[[Attention Is All You Need (Transformer)]] || 2017/06/12 || [[arxiv:1706.03762]] || [[Natural Language Processing]] || [[Google]] || 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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|[[Deep contextualized word representations (ELMo)]] || 2018/02/15 || [[arxiv:1802.05365]] || [[Natural Language Processing]] ||  || [[ELMo]]
|[[Deep contextualized word representations (ELMo)]] || 2018/02/15 || [[arxiv:1802.05365]] || [[Natural Language Processing]] ||  || [[ELMo]]
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|-
|[[GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding]] || 2018/04/20 || [[arxiv:1804.07461]] || [[Natural Language Processing]] ||  || [[GLUE]]
|[[GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding]] || 2018/04/20 || [[arxiv:1804.07461]]<br>[https://gluebenchmark.com/ website] || [[Natural Language Processing]] ||  || [[GLUE]]
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|[[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]] || 2018/10/11 || [[arxiv:1810.04805]] || [[Natural Language Processing]] || [[Google]] || [[BERT]]
|[[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]] || 2018/10/11 || [[arxiv:1810.04805]] || [[Natural Language Processing]] || [[Google]] || [[BERT]]
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|[[Language Models are Few-Shot Learners (GPT-3)]] || 2020/05/28 || [[arxiv:2005.14165]] || [[Natural Language Processing]] || [[OpenAI]] || [[GPT-3]]
|[[Language Models are Few-Shot Learners (GPT-3)]] || 2020/05/28 || [[arxiv:2005.14165]] || [[Natural Language Processing]] || [[OpenAI]] || [[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]] || [[Computer Vision]] ||  || [[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] || [[Computer Vision]] ||  || [[CLIP]]
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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]] || [[Computer Vision]] ||  || [[MobileViT]]
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|[[Block-Recurrent Transformers]] || 2022/03/11 || [[arxiv:2203.07852]] ||  ||  ||  
|[[Block-Recurrent Transformers]] || 2022/03/11 || [[arxiv:2203.07852]] ||  ||  ||  

Revision as of 19:38, 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 Natural Language Processing 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 Natural Language Processing 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 Google 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 Natural Language Processing Google 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 Natural Language Processing OpenAI GPT
Deep contextualized word representations (ELMo) 2018/02/15 arxiv:1802.05365 Natural Language Processing ELMo
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding 2018/04/20 arxiv:1804.07461
website		 Natural Language Processing GLUE
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding 2018/10/11 arxiv:1810.04805 Natural Language Processing Google 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 Natural Language Processing OpenAI GPT-3
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT) 2020/10/22 arxiv:2010.11929 Computer Vision Vision Transformer (ViT)
Learning Transferable Visual Models From Natural Language Supervision (CLIP) 2021/02/26 arxiv:2103.00020
OpenAI Blog		 Computer Vision CLIP
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer 2021/10/05 arxiv:2110.02178 Computer Vision 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
SingSong: Generating musical accompaniments from singing 2023/01/30 arxiv:2301.12662
blog post		 Audio SingSong
MusicLM: Generating Music From Text 2023/01/26 arxiv:2301.11325
blog post		 Audio Google MusicLM
Mastering Diverse Domains through World Models (DreamerV3) 2023/01/10 arxiv:2301.04104v1
blogpost		 DeepMind DreamerV3
Muse: Text-To-Image Generation via Masked Generative Transformers 2023/01/02 arxiv:2301.00704
blog post		 Computer Vision Google Muse
Constitutional AI: Harmlessness from AI Feedback 2021/12/12 arxiv:2212.08073 Natural Language Processing Anthropic Constitutional AI, Claude
InstructPix2Pix: Learning to Follow Image Editing Instructions 2021/11/17 arxiv:2211.09800
Blog Post		 Computer Vision UC Berkley InstructPix2Pix
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