Papers: Difference between revisions

|[[ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)]] || 2012 || [https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf 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 || [https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf 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]]<br>[https://openai.com/blog/clip/ 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]]