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Generative adversarial networks (GANs) have great successes on synthesizing data.
Long short-term memory
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Earlier work this paper cites.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 1998
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Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David A McAllester, Satinder P Singh, Yishay Mansour, et al · 1999
Earlier work this paper cites.
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Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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The complete works of William Shakespeare
William Shakespeare · 2014
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