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On Minibatch Noise: Discrete-Time SGD, Overparametrization, and Bayes

Feb 10, 2021
Liu Ziyin, Kangqiao Liu, Takashi Mori, Masahito Ueda

* The first two authors contributed equally 

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Stochastic Gradient Descent with Large Learning Rate

Dec 17, 2020
Kangqiao Liu, Liu Ziyin, Masahito Ueda

* 26 pages, 6 figures. *First two authors contributed equally 

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Improved generalization by noise enhancement

Sep 28, 2020
Takashi Mori, Masahito Ueda

* 9 pages 

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Neural Networks Fail to Learn Periodic Functions and How to Fix It

Jun 15, 2020
Liu Ziyin, Tilman Hartwig, Masahito Ueda


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Is deeper better? It depends on locality of relevant features

May 26, 2020
Takashi Mori, Masahito Ueda

* 12 pages 

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Volumization as a Natural Generalization of Weight Decay

Apr 01, 2020
Liu Ziyin, Zihao Wang, Makoto Yamada, Masahito Ueda

* 18 pages, 20 figures 

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Learning Not to Learn in the Presence of Noisy Labels

Feb 16, 2020
Liu Ziyin, Blair Chen, Ru Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency, Masahito Ueda


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LaProp: a Better Way to Combine Momentum with Adaptive Gradient

Feb 12, 2020
Liu Ziyin, Zhikang T. Wang, Masahito Ueda


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Deep Reinforcement Learning Control of Quantum Cartpoles

Oct 24, 2019
Zhikang T. Wang, Yuto Ashida, Masahito Ueda

* 5+3 pages, 2 figures, 2+2 tables, 5 videos at an external link 

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Deep Gamblers: Learning to Abstain with Portfolio Theory

Jun 29, 2019
Liu Ziyin, Zhikang Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency, Masahito Ueda


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