Toward understanding the importance of noise in training neural networks M Zhou, T Liu, Y Li, D Lin, E Zhou, T Zhao
International Conference on Machine Learning, 7594-7602, 2019
98 2019 Explicit convergence rates of greedy and random quasi-Newton methods D Lin, H Ye, Z Zhang
Journal of Machine Learning Research 23 (162), 1-40, 2022
20 2022 Greedy and random quasi-newton methods with faster explicit superlinear convergence D Lin, H Ye, Z Zhang
Advances in Neural Information Processing Systems 34, 6646-6657, 2021
18 2021 Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis D Lin, Y Han, H Ye, Z Zhang
Advances in Neural Information Processing Systems 36, 2024
12 2024 Towards explicit superlinear convergence rate for SR1 H Ye, D Lin, X Chang, Z Zhang
Mathematical Programming 199 (1-2), 1273-1303, 2023
12 2023 Explicit superlinear convergence rates of Broyden's methods in nonlinear equations D Lin, H Ye, Z Zhang
arXiv preprint arXiv:2109.01974, 2021
11 2021 Greedy and random Broyden's methods with explicit superlinear convergence rates in nonlinear equations H Ye, D Lin, Z Zhang
arXiv preprint arXiv:2110.08572, 2021
10 2021 Explicit superlinear convergence rates of the SR1 algorithm H Ye, D Lin, Z Zhang, X Chang
arXiv preprint arXiv:2105.07162, 2021
7 2021 On the landscape of one-hidden-layer sparse networks and beyond D Lin, R Sun, Z Zhang
Artificial Intelligence 309, 103739, 2022
5 2022 Faster directional convergence of linear neural networks under spherically symmetric data D Lin, R Sun, Z Zhang
Advances in Neural Information Processing Systems 34, 4647-4660, 2021
4 2021 Optimal quantization for batch normalization in neural network deployments and beyond D Lin, P Sun, G Xie, S Zhou, Z Zhang
arXiv preprint arXiv:2008.13128, 2020
4 2020 Global convergence analysis of deep linear networks with a one-neuron layer K Chen, D Lin, Z Zhang
arXiv preprint arXiv:2201.02761, 2022
2 2022 Towards better generalization: Bp-svrg in training deep neural networks H Jin, D Lin, Z Zhang
arXiv preprint arXiv:1908.06395, 2019
2 2019 On Non-local Convergence Analysis of Deep Linear Networks K Chen, D Lin, Z Zhang
International Conference on Machine Learning, 3417-3443, 2022
1 2022 Anderson Acceleration Without Restart: A Novel Method with H Ye, D Lin, X Chang, Z Zhang
arXiv preprint arXiv:2403.16734, 2024
2024 On the Convergence of Policy in Unregularized Policy Mirror Descent D Lin, Z Zhang
arXiv preprint arXiv:2205.08176, 2022
2022 Meta-Regularization: An Approach to Adaptive Choice of the Learning Rate in Gradient Descent G Xie, H Jin, D Lin, Z Zhang
arXiv preprint arXiv:2104.05447, 2021
2021 
Zhihua ZhangProfessor of Computer Science, Shanghai Jiao Tong UniversityVerifisert e-postadresse på zju.edu.cn
