Papers: Difference between revisions

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|[[Efficient Estimation of Word Representations in Vector Space (Word2Vec)]] || 2013/01/16 || [[arxiv:1301.3781]] || [[Natural Language Processing]] ||  || [[Word2Vec]]
|[[Efficient Estimation of Word Representations in Vector Space (Word2Vec)]] || 2013/01/16 || [[arxiv:1301.3781]] || [[Natural Language Processing]] ||  || [[Word2Vec]]
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|[[Playing Atari with Deep Reinforcement Learning (DQN)]] || 2013/12/19 || [[arxiv:1312.5602]] ||  ||  || [[DQN]]<br>[[Deep Q-Learning]]
|[[Playing Atari with Deep Reinforcement Learning (DQN)]] || 2013/12/19 || [[arxiv:1312.5602]] ||  ||  || [[DQN]] ([[Deep Q-Learning]])
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|[[Generative Adversarial Networks (GAN)]] || 2014/06/10 || [[arxiv:1406.2661]] ||  ||  || [[GAN]]<br>[[Generative Adversarial Network]]
|[[Generative Adversarial Networks (GAN)]] || 2014/06/10 || [[arxiv:1406.2661]] ||  ||  || [[GAN]] ([[Generative Adversarial Network]])
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|-
|[[Very Deep Convolutional Networks for Large-Scale Image Recognition (VGGNet)]] || 2014/09/04 || [[arxiv:1409.1556]] || [[Computer Vision]] ||  || [[VGGNet]]
|[[Very Deep Convolutional Networks for Large-Scale Image Recognition (VGGNet)]] || 2014/09/04 || [[arxiv:1409.1556]] || [[Computer Vision]] ||  || [[VGGNet]]
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|[[Attention Is All You Need (Transformer)]] || 2017/06/12 || [[arxiv:1706.03762]] || [[Natural Language Processing]] || [[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]]<br>[[Proximal Policy Optimization]]
|[[Proximal Policy Optimization Algorithms (PPO)]] || 2017/07/20 || [[arxiv:1707.06347]] ||  ||  || [[PPO]] ([[Proximal Policy Optimization]])
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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] || [[Natural Language Processing]] || [[OpenAI]] || [[GPT]]<br>[[Generative Pre-Training]]
|[[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] || [[Natural Language Processing]] || [[OpenAI]] || [[GPT]] ([[Generative Pre-Training]])
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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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|[[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]]<br>[https://github.com/google-research/vision_transformer GitHub] || [[Computer Vision]] || [[Google]] || [[Vision Transformer]] ([[ViT]])
|[[An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT)]] || 2020/10/22 || [[arxiv:2010.11929]]<br>[https://github.com/google-research/vision_transformer GitHub] || [[Computer Vision]] || [[Google]] || [[ViT]] ([[Vision Transformer]])
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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/ Blog Post] || [[Computer Vision]] || [[OpenAI]] || [[CLIP]] ([[Contrastive Language-Image Pre-Training]])
|[[Learning Transferable Visual Models From Natural Language Supervision (CLIP)]] || 2021/02/26 || [[arxiv:2103.00020]]<br>[https://openai.com/blog/clip/ Blog Post] || [[Computer Vision]] || [[OpenAI]] || [[CLIP]] ([[Contrastive Language-Image Pre-Training]])

Revision as of 23:29, 7 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 Computer Vision 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 (Deep Q-Learning)
Generative Adversarial Networks (GAN) 2014/06/10 arxiv:1406.2661 GAN (Generative Adversarial Network)
Very Deep Convolutional Networks for Large-Scale Image Recognition (VGGNet) 2014/09/04 arxiv:1409.1556 Computer Vision 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:1512.03385 Computer Vision ResNet
Going Deeper with Convolutions (GoogleNet) 2015/12/10 arxiv:1409.4842 Computer Vision 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 (Proximal Policy Optimization)
Improving Language Understanding by Generative Pre-Training (GPT) 2018 paper source Natural Language Processing OpenAI GPT (Generative Pre-Training)
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
github		 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
GitHub		 Computer Vision Google ViT (Vision Transformer)
Learning Transferable Visual Models From Natural Language Supervision (CLIP) 2021/02/26 arxiv:2103.00020
Blog Post		 Computer Vision OpenAI CLIP (Contrastive Language-Image Pre-Training)
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer 2021/10/05 arxiv:2110.02178
GitHub		 Computer Vision Apple MobileViT
LaMDA: Language Models for Dialog Applications 2022/01/20 arxiv:2201.08239
Blog Post		 Natural Language Processing Google LaMDA
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
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (Imagen) 2022/05/23 arxiv:2205.11487
Blog Post		 Computer Vision Google Imagen
PaLM: Scaling Language Modeling with Pathways 2022/04/05 arxiv:2204.02311
Blog Post		 Natural Language Processing Google PaLM
Constitutional AI: Harmlessness from AI Feedback 2021/12/12 arxiv:2212.08073 Natural Language Processing Anthropic Constitutional AI, Claude
Improving language models by retrieving from trillions of tokens (RETRO) 2021/12/08 arxiv:2112.04426
Blog post		 Natural Language Processing OpenAI RETRO (Retrieval Enhanced Transformer)
InstructPix2Pix: Learning to Follow Image Editing Instructions 2021/11/17 arxiv:2211.09800
Blog Post		 Computer Vision UC Berkley InstructPix2Pix
REALM: Retrieval-Augmented Language Model Pre-Training 2020/02/10 arxiv:2002.08909
Blog Post		 Natural Language Processing Google REALM
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