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] || [[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 || [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]]) ||

|[[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]]<br>[https://gluebenchmark.com/ website] || [[Natural Language Processing]] ||  || [[GLUE]] ([[General Language Understanding Evaluation]]) ||

|[[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]] || 2018/10/11 || [[arxiv:1810.04805]] || [[Natural Language Processing]] || [[Google]] || [[BERT]] ([[Bidirectional Encoder Representations from Transformers]]) ||

|[[Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context]] || 2019/01/09 || [[arxiv:1901.02860]]<br>[https://github.com/kimiyoung/transformer-xl 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]]<br>[https://github.com/google-research/vision_transformer GitHub] || [[Computer Vision]] || [[Google]] || [[ViT]] ([[Vision Transformer]]) ||

|[[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]]) ||

|[[MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer]] || 2021/10/05 || [[arxiv:2110.02178]]<br>[https://github.com/apple/ml-cvnets GitHub] || [[Computer Vision]] || [[Apple]] || [[MobileViT]] ||

|[[LaMDA: Language Models for Dialog Applications]] || 2022/01/20 || [[arxiv:2201.08239]]<br>[https://blog.google/technology/ai/lamda/ Blog Post] || [[Natural Language Processing]] || [[Google]] || [[LaMDA]] ([[Language Models for Dialog Applications]]) ||

|[[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]] ([[Self-Taught Reasoner]]) ||