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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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Cross-Modal Generalization: Learning in Low Resource Modalities via Meta-Alignment

Dec 04, 2020
Paul Pu Liang, Peter Wu, Liu Ziyin, Louis-Philippe Morency, Ruslan Salakhutdinov


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An Investigation of how Label Smoothing Affects Generalization

Oct 23, 2020
Blair Chen, Liu Ziyin, Zihao Wang, Paul Pu Liang


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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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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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Think Locally, Act Globally: Federated Learning with Local and Global Representations

Jan 06, 2020
Paul Pu Liang, Terrance Liu, Liu Ziyin, Ruslan Salakhutdinov, Louis-Philippe Morency

* Workshop on Federated Learning for Data Privacy and Confidentiality, NeurIPS 2019, Vancouver, Canada 

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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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