Masahito Ueda - RankMe

31 papers A* 8Journal 23

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Symbolic equation solving via reinforcement learning. Neurocomputing Lennart Dabelow, Masahito Ueda
2024	J	`jnl`	Symbolic Equation Solving via Reinforcement Learning. CoRR Lennart Dabelow, Masahito Ueda
2023	J	`jnl`	Law of Balance and Stationary Distribution of Stochastic Gradient Descent. CoRR Liu Ziyin, Hongchao Li, Masahito Ueda
2023	J	`jnl`	The Probabilistic Stability of Stochastic Gradient Descent. CoRR Liu Ziyin, Botao Li, Tomer Galanti, Masahito Ueda
2023	A*	`conf`	What shapes the loss landscape of self supervised learning? ICLR Liu Ziyin, Ekdeep Singh Lubana, Masahito Ueda, Hidenori Tanaka
2022	A*	`conf`	Convergent and Efficient Deep Q Learning Algorithm. ICLR Zhikang T. Wang, Masahito Ueda
2022	J	`jnl`	Exact Phase Transitions in Deep Learning. CoRR Liu Ziyin, Masahito Ueda
2022	J	`jnl`	Interplay between depth of neural networks and locality of target functions. CoRR Takashi Mori, Masahito Ueda
2022	A*	`conf`	Power-Law Escape Rate of SGD. ICML Takashi Mori, Liu Ziyin, Kangqiao Liu, Masahito Ueda
2022	A*	`conf`	SGD Can Converge to Local Maxima. ICLR Liu Ziyin, Botao Li, James B. Simon, Masahito Ueda
2022	J	`jnl`	Stochastic Neural Networks with Infinite Width are Deterministic. CoRR Liu Ziyin, Hanlin Zhang, Xiangming Meng, Yuting Lu, Eric P. Xing, Masahito Ueda
2022	A*	`conf`	Strength of Minibatch Noise in SGD. ICLR Liu Ziyin, Kangqiao Liu, Takashi Mori, Masahito Ueda
2022	J	`jnl`	Three Learning Stages and Accuracy-Efficiency Tradeoff of Restricted Boltzmann Machines. CoRR Lennart Dabelow, Masahito Ueda
2022	J	`jnl`	What shapes the loss landscape of self-supervised learning? CoRR Liu Ziyin, Ekdeep Singh Lubana, Masahito Ueda, Hidenori Tanaka
2021	J	`jnl`	A Convergent and Efficient Deep Q Network Algorithm. CoRR Zhikang T. Wang, Masahito Ueda
2021	J	`jnl`	Logarithmic landscape and power-law escape rate of SGD. CoRR Takashi Mori, Liu Ziyin, Kangqiao Liu, Masahito Ueda
2021	A*	`conf`	Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent. ICML Kangqiao Liu, Liu Ziyin, Masahito Ueda
2021	J	`jnl`	On Minibatch Noise: Discrete-Time SGD, Overparametrization, and Bayes. CoRR Liu Ziyin, Kangqiao Liu, Takashi Mori, Masahito Ueda
2021	J	`jnl`	SGD May Never Escape Saddle Points. CoRR Liu Ziyin, Botao Li, Masahito Ueda
2020	J	`jnl`	Improved generalization by noise enhancement. CoRR Takashi Mori, Masahito Ueda
2020	J	`jnl`	Is deeper better? It depends on locality of relevant features. CoRR Takashi Mori, Masahito Ueda
2020	J	`jnl`	LaProp: a Better Way to Combine Momentum with Adaptive Gradient. CoRR Liu Ziyin, Zhikang Wang, Masahito Ueda
2020	J	`jnl`	Learning Not to Learn in the Presence of Noisy Labels. CoRR Liu Ziyin, Blair Chen, Ru Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency, Masahito Ueda
2020	A*	`conf`	Neural Networks Fail to Learn Periodic Functions and How to Fix It. NeurIPS Liu Ziyin, Tilman Hartwig, Masahito Ueda
2020	J	`jnl`	Neural Networks Fail to Learn Periodic Functions and How to Fix It. CoRR Liu Ziyin, Tilman Hartwig, Masahito Ueda
2020	J	`jnl`	Stochastic Gradient Descent with Large Learning Rate. CoRR Kangqiao Liu, Liu Ziyin, Masahito Ueda
2020	J	`jnl`	Volumization as a Natural Generalization of Weight Decay. CoRR Liu Ziyin, Zihao Wang, Makoto Yamada, Masahito Ueda
2019	A*	`conf`	Deep Gamblers: Learning to Abstain with Portfolio Theory. NeurIPS Liu Ziyin, Zhikang Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency, Masahito Ueda
2019	J	`jnl`	Deep Gamblers: Learning to Abstain with Portfolio Theory. CoRR Liu Ziyin, Zhikang Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency, Masahito Ueda
2019	J	`jnl`	Deep Reinforcement Learning Control of Quantum Cartpoles. CoRR Zhikang Wang, Yuto Ashida, Masahito Ueda
2003	J	`jnl`	Einstein-Podolsky-Rosen correlation seen from moving observers. Quantum Inf. Comput. Hiroaki Terashima, Masahito Ueda