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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] ||  || [[AlexNet]]
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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]] || [[Word2Vec]]
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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]] ||  || [[DQN]]
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|[[Generative Adversarial Networks (GAN)]] || 2014/06/10 || [[arxiv:1406.2661]] ||  ||  
|[[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]] ||  ||  
|[[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]] ||  ||  
|[[Sequence to Sequence Learning with Neural Networks (Seq2Seq)]] || 2014/09/10 || [[arxiv:1409.3215]] ||  || [[Seq2Seq]]
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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]] ||  || [[ResNet]]
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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]] ||  || [[GoogleNet]]
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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]] ||  || [[A3C]]
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|[[WaveNet: A Generative Model for Raw Audio]] || 2016/09/12 || [[arxiv:1609.03499]] || [[Audio]] ||  
|[[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]] ||  ||  
|[[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]] ||  
|[[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: 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]]

Revision as of 02:44, 6 February 2023

Important Papers

Name Submission
Date 		Source Type 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
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 2019/01/09 arxiv:1901.02860 Attentive Language Models Beyond a Fixed-Length Context
Language Models are Few-Shot Learners (GPT-3) 2020/05/28 arxiv:2005.14165 NLP GPT-3
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

Other Papers

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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