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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] ||  ||  || [[AlexNet]]
|[[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]] ||  || [[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]]
|[[Playing Atari with Deep Reinforcement Learning (DQN)]] || 2013/12/19 || [[arxiv:1312.5602]] ||  ||  || [[DQN]]
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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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|[[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]] || [[OpenAI]] || [[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] || [[Natural Language Processing]] || [[OpenAI]] || [[GPT]]
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|[[Deep contextualized word representations (ELMo)]] || 2018/02/15 || [[arxiv:1802.05365]] || [[NLP]] ||  || [[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]] || [[NLP]] ||  || [[GLUE]]
|[[GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding]] || 2018/04/20 || [[arxiv:1804.07461]] || [[Natural Language Processing]] ||  || [[GLUE]]
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|[[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]] || 2018/10/11 || [[arxiv:1810.04805]] || [[NLP]] || [[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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|[[Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context]] || 2019/01/09 || [[arxiv:1901.02860]] ||  ||  || [[Transformer-XL]]
|[[Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context]] || 2019/01/09 || [[arxiv:1901.02860]] ||  ||  || [[Transformer-XL]]
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|[[Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model (MuZero)]] || 2019/11/19 || [[arxiv:1911.08265]] ||  ||  || [[MuZero]]
|[[Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model (MuZero)]] || 2019/11/19 || [[arxiv:1911.08265]] ||  ||  || [[MuZero]]
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|[[Language Models are Few-Shot Learners (GPT-3)]] || 2020/05/28 || [[arxiv:2005.14165]] || [[NLP]] || [[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]] ||  ||  || [[Vision Transformer]] ([[ViT]])
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