| Reshaped wirtinger flow for solving quadratic system of equations H Zhang, Y Liang Advances in Neural Information Processing Systems, 2622-2630, 2016 | 98 | 2016 |
| Provable non-convex phase retrieval with outliers: Median truncatedwirtinger flow H Zhang, Y Chi, Y Liang International conference on machine learning, 1022-1031, 2016 | 83 | 2016 |
| A nonconvex approach for phase retrieval: Reshaped wirtinger flow and incremental algorithms H Zhang, Y Zhou, Y Liang, Y Chi The Journal of Machine Learning Research 18 (1), 5164-5198, 2017 | 53 | 2017 |
| 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 | 28 | 2014 |
| Reshaped Wirtinger flow and incremental algorithm for solving quadratic system of equations H Zhang, Y Zhou, Y Liang, Y Chi arXiv preprint arXiv:1605.07719, 2016 | 25 | 2016 |
| 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 | 24 | 2019 |
| On layer normalization in the transformer architecture R Xiong, Y Yang, D He, K Zheng, S Zheng, C Xing, H Zhang, Y Lan, ... arXiv preprint arXiv:2002.04745, 2020 | 23 | 2020 |
| Geometrical properties and accelerated gradient solvers of non-convex phase retrieval Y Zhou, H Zhang, Y Liang 2016 54th Annual Allerton Conference on Communication, Control, and …, 2016 | 19 | 2016 |
| Analysis of robust PCA via local incoherence H Zhang, Y Zhou, Y Liang Advances in Neural Information Processing Systems, 1819-1827, 2015 | 17 | 2015 |
| Median-truncated nonconvex approach for phase retrieval with outliers H Zhang, Y Chi, Y Liang IEEE Transactions on Information Theory 64 (11), 7287-7310, 2018 | 16 | 2018 |
| 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 | 13 | 2020 |
| Multi-key generation over a cellular model with a helper H Zhang, Y Liang, L Lai, SS Shitz IEEE Transactions on Information Theory 63 (6), 3804-3822, 2017 | 12 | 2017 |
| Secret key capacity: Talk or keep silent? H Zhang, Y Liang, L Lai 2015 IEEE International Symposium on Information Theory (ISIT), 291-295, 2015 | 12 | 2015 |
| Training over-parameterized deep resnet is almost as easy as training a two-layer network H Zhang, D Yu, W Chen, TY Liu arXiv preprint arXiv:1903.07120, 2019 | 11 | 2019 |
| Capacity control of relu neural networks by basis-path norm S Zheng, Q Meng, H Zhang, W Chen, N Yu, TY Liu Proceedings of the AAAI Conference on Artificial Intelligence 33, 5925-5932, 2019 | 10 | 2019 |
| -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 | 10 | 2018 |
| Block-diagonal hessian-free optimization for training neural networks H Zhang, C Xiong, J Bradbury, R Socher arXiv preprint arXiv:1712.07296, 2017 | 9 | 2017 |
| On the local Hessian in back-propagation H Zhang, W Chen, TY Liu Advances in Neural Information Processing Systems, 6520-6530, 2018 | 8 | 2018 |
| Key capacity region for a cellular source model H Zhang, Y Liang, L Lai 2014 IEEE Information Theory Workshop (ITW 2014), 321-325, 2014 | 8 | 2014 |
| 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 | 7 | 2020 |
Yuejie ChiCarnegie Mellon UniversityVerified email at cmu.edu
