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|[[Very Deep Convolutional Networks for Large-Scale Image Recognition (VGGNet)]] || 2014/09/04 || [[arxiv:409.1556]] || || || [[VGGNet]] | |[[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]] || [[Natural Language Processing]] || || [[Seq2Seq]] | ||
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|[[Adam: A Method for Stochastic Optimization)]] || 2014/12/22 || [[arxiv:1412.6980]] || || || [[Adam]] | |[[Adam: A Method for Stochastic Optimization)]] || 2014/12/22 || [[arxiv:1412.6980]] || || || [[Adam]] | ||
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|[[WaveNet: A Generative Model for Raw Audio]] || 2016/09/12 || [[arxiv:1609.03499]] || [[Audio]] || || [[WaveNet]] | |[[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]] || | |[[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]] | |[[Proximal Policy Optimization Algorithms (PPO)]] || 2017/07/20 || [[arxiv:1707.06347]] || || || [[PPO]] | ||
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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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|[[GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding]] || 2018/04/20 || [[arxiv:1804.07461]] || [[Natural Language Processing]] || || [[GLUE]] | |[[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]] | ||
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|[[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]] || 2018/10/11 || [[arxiv:1810.04805]] || [[Natural Language Processing]] || [[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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|[[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]] || | |[[An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT)]] || 2020/10/22 || [[arxiv:2010.11929]] || [[Computer Vision]] || || [[Vision Transformer]] ([[ViT]]) | ||
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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/ OpenAI Blog] || | |[[Learning Transferable Visual Models From Natural Language Supervision (CLIP)]] || 2021/02/26 || [[arxiv:2103.00020]]<br>[https://openai.com/blog/clip/ OpenAI Blog] || [[Computer Vision]] || || [[CLIP]] | ||
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|[[MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer]] || 2021/10/05 || [[arxiv:2110.02178]] || | |[[MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer]] || 2021/10/05 || [[arxiv:2110.02178]] || [[Computer Vision]] || || [[MobileViT]] | ||
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|[[Block-Recurrent Transformers]] || 2022/03/11 || [[arxiv:2203.07852]] || || || | |[[Block-Recurrent Transformers]] || 2022/03/11 || [[arxiv:2203.07852]] || || || |