Следене
Zheng Xu
Заглавие
Позовавания
Позовавания
Година
Advances and open problems in federated learning
P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ...
arXiv preprint arXiv:1912.04977, 2019
17582019
Visualizing the loss landscape of neural nets
H Li, Z Xu, G Taylor, C Studer, T Goldstein
Advances in Neural Information Processing Systems, 6391-6401, 2018
9622018
Adversarial Training for Free!
A Shafahi, M Najibi, A Ghiasi, Z Xu, J Dickerson, C Studer, LS Davis, ...
Conference on Neural Information Processing Systems (NeurIPS), 2019
6102019
Training neural networks without gradients: A scalable ADMM approach
G Taylor, R Burmeister, Z Xu, B Singh, A Patel, T Goldstein
International Conference on Machine Learning (ICML), 2722-2731, 2016
2312016
Exploiting Low-Rank Structure from Latent Domains for Domain Generalization
Z Xu, W Li, L Niu, D Xu
European Conference on Computer Vision (ECCV), 628-643, 2014
1622014
Training Quantized Nets: A Deeper Understanding
H Li, S De, Z Xu, C Studer, H Samet, T Goldstein
Conference on Neural Information Processing Systems (NIPS), 2017
1582017
Towards Perceptual Image Dehazing by Physics-based Disentanglement and Adversarial Training
X Yang, Z Xu, J Luo
AAAI Conference on Artificial Intelligence (AAAI), 2018
1352018
Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks
Z Xu, YC Hsu, J Huang
arXiv preprint arXiv:1709.00513, 2017
115*2017
Universal Adversarial Training
A Shafahi, M Najibi, Z Xu, J Dickerson, LS Davis, T Goldstein
AAAI Conference on Artificial Intelligence (AAAI), 2020
962020
Adaptive ADMM with Spectral Penalty Parameter Selection
Z Xu, MAT Figueiredo, T Goldstein
International Conference on Artificial Intelligence and Statistics (AISTATS), 2017
942017
Domain Generalization and Adaptation using Low Rank Exemplar SVMs
W Li, Z Xu, D Xu, D Dai, LV Gool
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017
912017
Stabilizing Adversarial Nets With Prediction Methods
A Yadav, S Shah, Z Xu, D Jacobs, T Goldstein
International Conference on Learning Representations (ICLR), 2018
852018
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent
KA Sankararaman, S De, Z Xu, WR Huang, T Goldstein
International Conference on Machine Learning (ICML), 2020
65*2020
A Field Guide to Federated Optimization
J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ...
arXiv preprint arXiv:2107.06917, 2021
612021
Mining Visualness
Z Xu, XJ Wang, CW Chen
IEEE International Conference on Multimedia and Expo (ICME), 2013
60*2013
Adaptive Consensus ADMM for Distributed Optimization
Z Xu, G Taylor, H Li, M Figueiredo, X Yuan, T Goldstein
International Conference on Machine Learning (ICML), 2017
552017
Learning to Cluster for Proposal-Free Instance Segmentation
YC Hsu, Z Xu, Z Kira, J Huang
2018 International Joint Conference on Neural Networks (IJCNN), 1-8, 2018
422018
Strong Baseline for Single Image Dehazing with Deep Features and Instance Normalization
Z Xu, X Yang, X Li, X Sun
British Machine Vision Conference (BMVC), 2018
36*2018
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation
Z Xu, M Figueiredo, X Yuan, C Studer, T Goldstein
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
362017
An Empirical Study of ADMM for Nonconvex Problems
Z Xu, S De, MAT Figueiredo, C Studer, T Goldstein
NIPS workshop on nonconvex optimization, 2016
35*2016
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