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Exposure bias refers to the train-test discrepancy that seemingly arises when an autoregressive generative model uses only ground-truth contexts at training time but generated ones at test time.
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Matt J. Kusner, Yu Sun, Nicholas I. Kolkin, and Kilian Q. Weinberger. 2015 · 2015
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Irina Higgins, Loïc Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew M Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017 · 2017
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Z. Hu, Z. Yang, Liang X., R. Salakhutdinov, and E. R. Xing. 2017 · 2017
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Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. 2016 · 2016
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Globally normalized transition-based neural networks
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Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin, and Simon Lacoste-Julien. 2018 · 2018
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Sidi Lu, Yaoming Zhu, Weinan Zhang, Jun Wang, and Yong Yu. 2018 · 2018
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Tighter variational bounds are not necessarily better
Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, and Yee Whye Teh. 2018 · 2018
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Deep state space models for unconditional word generation
Florian Schmidt and Thomas Hofmann. 2018 · 2018
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Xiaoyu Shen, Hui Su, Shuzi Niu, and Vera Demberg. 2018 · 2018
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Bowen Tan, Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, and Eric P. Xing. 2018 · 2018
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Michael Tschannen, Olivier Bachem, and Mario Lucic. 2018 · 2018
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Cheng Zhang, Judith Butepage, Hedvig Kjellstrom, and Stephan Mandt. 2018 · 2018
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Deep reinforcement learning with distributional semantic rewards for abstractive summarization
Siyao Li, Deren Lei, Pengda Qin, and William Wang. 2019 · 2019
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