Huishuai Zhang
Huishuai Zhang
Vahvistettu sähköpostiosoite verkkotunnuksessa - Kotisivu
On layer normalization in the transformer architecture
R Xiong, Y Yang, D He, K Zheng, S Zheng, C Xing, H Zhang, Y Lan, ...
International Conference on Machine Learning, 10524-10533, 2020
A nonconvex approach for phase retrieval: Reshaped wirtinger flow and incremental algorithms
H Zhang, Y Liang, Y Chi
Journal of Machine Learning Research 18 (141), 1-35, 2017
Differentially private fine-tuning of language models
D Yu, S Naik, A Backurs, S Gopi, HA Inan, G Kamath, J Kulkarni, YT Lee, ...
arXiv preprint arXiv:2110.06500, 2021
Provable non-convex phase retrieval with outliers: Median truncatedwirtinger flow
H Zhang, Y Chi, Y Liang
International conference on machine learning, 1022-1031, 2016
Block-diagonal hessian-free optimization for recurrent and convolutional neural networks
H Zhang, C Xiong
US Patent 11,386,327, 2022
Do not let privacy overbill utility: Gradient embedding perturbation for private learning
D Yu, H Zhang, W Chen, TY Liu
arXiv preprint arXiv:2102.12677, 2021
Large scale private learning via low-rank reparametrization
D Yu, H Zhang, W Chen, J Yin, TY Liu
International Conference on Machine Learning, 12208-12218, 2021
Sgd converges to global minimum in deep learning via star-convex path
Y Zhou, J Yang, H Zhang, Y Liang, V Tarokh
arXiv preprint arXiv:1901.00451, 2019
Availability attacks create shortcuts
D Yu, H Zhang, W Chen, J Yin, TY Liu
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022
Adaptive inertia: Disentangling the effects of adaptive learning rate and momentum
Z Xie, X Wang, H Zhang, I Sato, M Sugiyama
International conference on machine learning, 24430-24459, 2022
How does data augmentation affect privacy in machine learning?
D Yu, H Zhang, W Chen, J Yin, TY Liu
Proceedings of the AAAI Conference on Artificial Intelligence 35 (12), 10746 …, 2021
Understanding generalization error of SGD in nonconvex optimization
Y Zhou, Y Liang, H Zhang
Machine Learning, 1-31, 2022
Gradient perturbation is underrated for differentially private convex optimization
D Yu, H Zhang, W Chen, TY Liu, J Yin
arXiv preprint arXiv:1911.11363, 2019
Exploring the limits of differentially private deep learning with group-wise clipping
J He, X Li, D Yu, H Zhang, J Kulkarni, YT Lee, A Backurs, N Yu, J Bian
arXiv preprint arXiv:2212.01539, 2022
Convergence of distributed stochastic variance reduced methods without sampling extra data
S Cen, H Zhang, Y Chi, W Chen, TY Liu
IEEE Transactions on Signal Processing 68, 3976-3989, 2020
Non-convex low-rank matrix recovery with arbitrary outliers via median-truncated gradient descent
Y Li, Y Chi, H Zhang, Y Liang
Information and Inference: A Journal of the IMA 9 (2), 289-325, 2020
-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space
Q Meng, S Zheng, H Zhang, W Chen, ZM Ma, TY Liu
arXiv preprint arXiv:1802.03713, 2018
Normalized/clipped sgd with perturbation for differentially private non-convex optimization
X Yang, H Zhang, W Chen, TY Liu
arXiv preprint arXiv:2206.13033, 2022
The capacity region of the source-type model for secret key and private key generation
H Zhang, L Lai, Y Liang, H Wang
IEEE Transactions on Information Theory 60 (10), 6389-6398, 2014
Stabilize deep ResNet with a sharp scaling factor
H Zhang, D Yu, M Yi, W Chen, TY Liu
Machine Learning 111 (9), 3359-3392, 2022
Järjestelmä ei voi suorittaa toimenpidettä nyt. Yritä myöhemmin uudelleen.
Artikkelit 1–20