Zhanxing Zhu
Zhanxing Zhu
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Cited by
Cited by
Spatio-temporal graph convolutional neural network: A deep learning framework for traffic forecasting
B Yu, H Yin, Z Zhu
International Joint Conference of Artificial Intelligence (IJCAI 2018), 2018
You only propagate once: Accelerating adversarial training via maximal principle
D Zhang, T Zhang, Y Lu, Z Zhu, B Dong
Advances in Neural Information Processing Systems, 2019, 2019
Reinforced continual learning
J Xu, Z Zhu
Advances in Neural Information Processing Systems (NeurIPS 2018), 899-908, 2018
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects
Z Zhu, J Wu, B Yu, L Wu, J Ma
International Conference on Machine Learning (ICML 2019), 7654-7663, 2019
Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes
L Wu, Z Zhu
The 34th International Conference on Machine Learning (ICML 2017 …, 2017
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes.
K Sun, Z Lin, Z Zhu
AAAI 2020, 5892-5899, 2020
Yet another text captcha solver: A generative adversarial network based approach
G Ye, Z Tang, D Fang, Z Zhu, Y Feng, P Xu, X Chen, Z Wang
Proceedings of the 2018 ACM SIGSAC conference on computer and communications …, 2018
Novel subgroups of patients with adult-onset diabetes in Chinese and US populations
X Zou, X Zhou, Z Zhu, L Ji
The Lancet Diabetes & Endocrinology 7 (1), 9-11, 2019
Interpreting Adversarially Trained Convolutional Neural Networks
T Zhang, Z Zhu
International Conference on Machine Learning (ICML 2019), 2019
Spatial-temporal fusion graph neural networks for traffic flow forecasting
M Li, Z Zhu
AAAI 2021, 2020
Learning with noise: Enhance distantly supervised relation extraction with dynamic transition matrix
B Luo, Y Feng, Z Wang, Z Zhu, S Huang, R Yan, D Zhao
ACL 2017, 2017
Towards understanding and improving the transferability of adversarial examples in deep neural networks
L Wu, Z Zhu
Asian Conference on Machine Learning, 837-850, 2020
Efficient Neural Architecture Search via Proximal Iterations.
Q Yao, J Xu, WW Tu, Z Zhu
AAAI 2020, 6664-6671, 2020
Covariance-controlled adaptive Langevin thermostat for large-scale Bayesian sampling
X Shang, Z Zhu, B Leimkuhler, AJ Storkey
NIPS 2015, 2015
Spatio-temporal manifold learning for human motions via long-horizon modeling
H Wang, ESL Ho, HPH Shum, Z Zhu
IEEE transactions on visualization and computer graphics 27 (1), 216-227, 2019
3d graph convolutional networks with temporal graphs: A spatial information free framework for traffic forecasting
B Yu, M Li, J Zhang, Z Zhu
arXiv preprint arXiv:1903.00919, 2019
St-unet: A spatio-temporal u-network for graph-structured time series modeling
B Yu, H Yin, Z Zhu
arXiv preprint arXiv:1903.05631, 2019
On the Noisy Gradient Descent that Generalizes as SGD
J Wu, W Hu, H Xiong, J Huan, V Braverman, Z Zhu
International Conference for Machine Learning (ICML 2020), 2020
Bayesian adversarial learning
N Ye, Z Zhu
Advances in Neural Information Processing Systems 31, 2018
A deep learning-based framework for conducting stealthy attacks in industrial control systems
C Feng, T Li, Z Zhu, D Chana
arXiv preprint arXiv:1709.06397, 2017
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