H
Huishuai Zhang
Researcher at Microsoft
Publications - 79
Citations - 1463
Huishuai Zhang is an academic researcher from Microsoft. The author has contributed to research in topics: Computer science & Gradient descent. The author has an hindex of 16, co-authored 62 publications receiving 941 citations. Previous affiliations of Huishuai Zhang include Syracuse University & Salesforce.com.
Papers
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On Layer Normalization in the Transformer Architecture
Ruibin Xiong,Yunchang Yang,Di He,Kai Zheng,Shuxin Zheng,Chen Xing,Huishuai Zhang,Yanyan Lan,Liwei Wang,Tie-Yan Liu +9 more
TL;DR: In this paper, the authors show that layer normalization is crucial to the performance of pre-LN Transformers and remove the warm-up stage for the training of Pre-LNs.
Proceedings Article
Reshaped Wirtinger Flow for Solving Quadratic System of Equations
Huishuai Zhang,Yingbin Liang +1 more
TL;DR: It is shown that for random Gaussian measurements, reshaped-WF enjoys geometric convergence to a global optimal point as long as the number of measurements is at the order of $\cO(n)$, where $n$ is the dimension of the unknown $\bx$.
Journal Article
A Nonconvex Approach for Phase Retrieval: Reshaped Wirtinger Flow and Incremental Algorithms
TL;DR: An incremental (stochastic) version of RWF (IRWF) is developed and connected with the randomized Kaczmarz method for phase retrieval and it is demonstrated that IRWF outperforms existing incremental as well as batch algorithms with experiments.
Posted Content
Provable Non-convex Phase Retrieval with Outliers: Median Truncated Wirtinger Flow
TL;DR: In this article, a non-convex gradient descent Wirtinger flow (WF) algorithm is proposed to recover the signal from a near-optimal number of measurements composed of i.i.d. Gaussian entries, up to a logarithmic factor, even when a constant portion of the measurements are corrupted by arbitrary outliers.
Proceedings Article
Provable non-convex phase retrieval with outliers: median truncated wirtinger flow
TL;DR: This paper develops a novel median-TWF algorithm that exploits robustness of sample median to resist arbitrary outliers in the initialization and the gradient update in each iteration, and shows that such a non-convex algorithm provably recovers the signal from a near-optimal number of measurements, even when a constant portion of the measurements are corrupted by arbitrary outlier.