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Making Pre-trained Language Models Better Few-shot Learners
(2) few-shot settings are realistic, as it is generally both easy to acquire a few annotations (e.g., 32 examples) and efficient to train on them; and (3) updating parameters typically leads to better perfor-mance. Inspired by GPT-3’s findings, we propose …
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arXiv:0706.3639v1 [cs.AI] 25 Jun 2007
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Deep Residual Learning for Image Recognition - …
arxiv.orgDeep Residual Learning for Image Recognition Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research fkahe, v-xiangz, v-shren, jiansung@microsoft.com
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arXiv:1301.3781v3 [cs.CL] 7 Sep 2013
arxiv.orgFor all the following models, the training complexity is proportional to O = E T Q; (1) where E is number of the training epochs, T is the number of …
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@google.com arXiv:1609.03499v2 [cs.SD] 19 Sep 2016
arxiv.orgwhere 1 <x t <1 and = 255. This non-linear quantization produces a significantly better reconstruction than a simple linear quantization scheme. …
A Tutorial on UAVs for Wireless Networks: …
arxiv.orgA Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems Mohammad Mozaffari 1, ... to UAVs in wireless communications is the work in …
Adversarial Generative Nets: Neural Network …
arxiv.orgAdversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer Carnegie Mellon University
Massive Exploration of Neural Machine Translation ...
arxiv.orgMassive Exploration of Neural Machine Translation Architectures Denny Britzy, Anna Goldie, Minh-Thang Luong, Quoc Le fdennybritz,agoldie,thangluong,qvlg@google.com Google Brain
Mastering Chess and Shogi by Self-Play with a …
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Going deeper with convolutions - arXiv
arxiv.orgGoing deeper with convolutions Christian Szegedy Google Inc. Wei Liu University of North Carolina, Chapel Hill Yangqing Jia Google Inc. Pierre Sermanet
Andrew G. Howard Menglong Zhu Bo Chen Dmitry ...
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