受强制性开放获取政策约束的文章 - Mingrui Liu了解详情
无法在其他位置公开访问的文章：1 篇
Spatiotemporal dynamics in a network composed of neurons with different excitabilities and excitatory coupling
WW Xiao, HG Gu, MR Liu
Science China Technological Sciences 59, 1943-1952, 2016
强制性开放获取政策： 国家自然科学基金委员会
可在其他位置公开访问的文章：19 篇
Weakly-convex–concave min–max optimization: provable algorithms and applications in machine learning
H Rafique, M Liu, Q Lin, T Yang
Optimization Methods and Software 37 (3), 1087-1121, 2022
强制性开放获取政策： US National Science Foundation
First-order convergence theory for weakly-convex-weakly-concave min-max problems
M Liu, H Rafique, Q Lin, T Yang
Journal of Machine Learning Research 22 (169), 1-34, 2021
强制性开放获取政策： US National Science Foundation
Improved Schemes for Episodic Memory-based Lifelong Learning
Y Guo*, M Liu*, T Yang, T Rosing
Advances in Neural Information Processing Systems 33, 2020
强制性开放获取政策： US National Science Foundation, US Department of Defense
A decentralized parallel algorithm for training generative adversarial nets
M Liu, W Zhang, Y Mroueh, X Cui, J Ross, T Yang, P Das
Advances in Neural Information Processing Systems 33, 11056-11070, 2020
强制性开放获取政策： US National Science Foundation
Understanding adamw through proximal methods and scale-freeness
Z Zhuang, M Liu, A Cutkosky, F Orabona
Transactions on machine learning research, 2022
强制性开放获取政策： US National Science Foundation
Fast Stochastic AUC Maximization with -Convergence Rate
M Liu, X Zhang, Z Chen, X Wang, T Yang
International Conference on Machine Learning, 3189-3197, 2018
强制性开放获取政策： US National Science Foundation
Robustness to unbounded smoothness of generalized signsgd
M Crawshaw, M Liu, F Orabona, W Zhang, Z Zhuang
Advances in neural information processing systems 35, 9955-9968, 2022
强制性开放获取政策： US National Science Foundation
ADMM without a fixed penalty parameter: Faster convergence with new adaptive penalization
Y Xu, M Liu, Q Lin, T Yang
Advances in neural information processing systems 30, 2017
强制性开放获取政策： US National Science Foundation
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
Z Guo, M Liu, Z Yuan, L Shen, W Liu, T Yang
International Conference on Machine Learning 2020, 2020
强制性开放获取政策： US National Science Foundation
Adaptive negative curvature descent with applications in non-convex optimization
M Liu, Z Li, X Wang, J Yi, T Yang
Advances in Neural Information Processing Systems, 4853-4862, 2018
强制性开放获取政策： US National Science Foundation
Will bilevel optimizers benefit from loops
K Ji, M Liu, Y Liang, L Ying
Advances in Neural Information Processing Systems 35, 3011-3023, 2022
强制性开放获取政策： US National Science Foundation
Generalization guarantee of SGD for pairwise learning
Y Lei, M Liu, Y Ying
Advances in neural information processing systems 34, 21216-21228, 2021
强制性开放获取政策： US National Science Foundation
Adaptive accelerated gradient converging methods under holderian error bound condition
M Liu, T Yang
Advances in Neural Information Processing Systems 30, 2016
强制性开放获取政策： US National Science Foundation
Fast rates of erm and stochastic approximation: Adaptive to error bound conditions
M Liu, X Zhang, L Zhang, R Jin, T Yang
Advances in Neural Information Processing Systems 30, 2018
强制性开放获取政策： US National Science Foundation
Fast composite optimization and statistical recovery in federated learning
Y Bao, M Crawshaw, S Luo, M Liu
International Conference on Machine Learning, 1508-1536, 2022
强制性开放获取政策： 国家自然科学基金委员会
On the last iterate convergence of momentum methods
X Li, M Liu, F Orabona
International Conference on Algorithmic Learning Theory, 699-717, 2022
强制性开放获取政策： US National Science Foundation
Faster online learning of optimal threshold for consistent F-measure optimization
X Zhang*, M Liu*, X Zhou, T Yang
Advances in Neural Information Processing Systems, 3889-3899, 2018
强制性开放获取政策： US National Science Foundation
On the initialization for convex-concave min-max problems
M Liu, F Orabona
International Conference on Algorithmic Learning Theory, 743-767, 2022
强制性开放获取政策： US National Science Foundation
Improved schemes for episodic memory based lifelong learning algorithm
Y Guo, M Liu, T Yang, T Rosing
Conference on Neural Information Processing Systems, 2020
强制性开放获取政策： US National Science Foundation, US Department of Defense
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