Zhanxing Zhu

ORCID

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 94

Top Venues

University of Edinburgh, UK (PHD)

Education History

University of Edinburgh, UK

PHD

Publications

Haoyi Xiong, Xuhong Li, Boyang Yu, Dongrui Wu, Zhanxing Zhu, Dejing Dou
Stochastic gradient descent with random label noises: doubly stochastic models and inference stabilizer. Mach. Learn. Sci. Technol. 5 (1) (2024)
Qianli Shen, Yezhen Wang, Zhouhao Yang, Xiang Li, Haonan Wang, Yang Zhang, Jonathan Scarlett, Zhanxing Zhu, Kenji Kawaguchi
Memory-Efficient Gradient Unrolling for Large-Scale Bi-level Optimization. CoRR (2024)
Mingxuan Yi, Zhanxing Zhu, Song Liu
MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows. ICML (2023)
Mingxuan Yi, Zhanxing Zhu, Song Liu
MonoFlow: Rethinking Divergence GANs via the Perspective of Differential Equations. CoRR (2023)
Ting Li, Jianguo Li, Zhanxing Zhu
Neural Lad: A Neural Latent Dynamics Framework for Times Series Modeling. NeurIPS (2023)
Bochen Lyu, Zhanxing Zhu
Implicit Bias of (Stochastic) Gradient Descent for Rank-1 Linear Neural Network. NeurIPS (2023)
Haoyi Xiong, Xuhong Li, Boyang Yu, Zhanxing Zhu, Dongrui Wu, Dejing Dou
Doubly Stochastic Models: Learning with Unbiased Label Noises and Inference Stability. CoRR (2023)
Nanyang Ye, Qianxiao Li, Xiao-Yun Zhou, Zhanxing Zhu
An Annealing Mechanism for Adversarial Training Acceleration. IEEE Trans. Neural Networks Learn. Syst. 34 (2) (2023)
Ke Sun, Bing Yu, Zhouchen Lin, Zhanxing Zhu
Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy. ACML (2023)
Deshan Gong, Zhanxing Zhu, Andrew J. Bulpitt, He Wang
Fine-grained differentiable physics: a yarn-level model for fabrics. CoRR (2022)
Ju Xu, Jin Ma, Xuesong Gao, Zhanxing Zhu
Adaptive Progressive Continual Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44 (10) (2022)
Deshan Gong, Zhanxing Zhu, Andrew J. Bulpitt, He Wang
Fine-grained Differentiable Physics: A Yarn-level Model for Fabrics. ICLR (2022)
Haoyi Xiong, Ruosi Wan, Jian Zhao, Zeyu Chen, Xingjian Li, Zhanxing Zhu, Jun Huan
GrOD: Deep Learning with Gradients Orthogonal Decomposition for Knowledge Transfer, Distillation, and Adversarial Training. ACM Trans. Knowl. Discov. Data 16 (6) (2022)
Bochen Lv, Zhanxing Zhu
Implicit Bias of Adversarial Training for Deep Neural Networks. ICLR (2022)
Quanshi Zhang, Tian Han, Lixin Fan, Zhanxing Zhu, Hang Su, Ying Nian Wu, Jie Ren, Hao Zhang
Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI. CoRR (2021)
Ke Sun, Zhanxing Zhu, Zhouchen Lin
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models. ICLR (2021)
Mengzhang Li, Zhanxing Zhu
Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting. AAAI (2021)
Nanyang Ye, Qianxiao Li, Xiao-Yun Zhou, Zhanxing Zhu
Amata: An Annealing Mechanism for Adversarial Training Acceleration. AAAI (2021)
Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu
Neural Approximate Sufficient Statistics for Implicit Models. ICLR (2021)
He Wang, Edmond S. L. Ho, Hubert P. H. Shum, Zhanxing Zhu
Spatio-Temporal Manifold Learning for Human Motions via Long-Horizon Modeling. IEEE Trans. Vis. Comput. Graph. 27 (1) (2021)
Ruosi Wan, Zhanxing Zhu, Xiangyu Zhang, Jian Sun
Spherical Motion Dynamics: Learning Dynamics of Normalized Neural Network using SGD and Weight Decay. NeurIPS (2021)
Nanyang Ye, Jingxuan Tang, Huayu Deng, Xiao-Yun Zhou, Qianxiao Li, Zhenguo Li, Guang-Zhong Yang, Zhanxing Zhu
Adversarial Invariant Learning. CVPR (2021)
Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization. ICML (2021)
Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization. CoRR (2021)
Kafeng Wang, Haoyi Xiong, Jiang Bian, Zhanxing Zhu, Qian Gao, Zhishan Guo, Cheng-Zhong Xu, Jun Huan, Dejing Dou
Sampling Sparse Representations with Randomized Measurement Langevin Dynamics. ACM Trans. Knowl. Discov. Data 15 (2) (2021)
Yutong Wang, Kunfeng Wang, Zhanxing Zhu, Feiyue Wang
Adversarial attacks on Faster R-CNN object detector. Neurocomputing 382 (2020)
Ruosi Wan, Zhanxing Zhu, Xiangyu Zhang, Jian Sun
Spherical Motion Dynamics of Deep Neural Networks with Batch Normalization and Weight Decay. CoRR (2020)
Mengzhang Li, Zhanxing Zhu
Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting. CoRR (2020)
Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu
On the Noisy Gradient Descent that Generalizes as SGD. ICML (2020)
Nanyang Ye, Qianxiao Li, Xiao-Yun Zhou, Zhanxing Zhu
Amata: An Annealing Mechanism for Adversarial Training Acceleration. CoRR (2020)
Bing Yu, Ke Sun, He Wang, Zhouchen Lin, Zhanxing Zhu
Classify and Generate Reciprocally: Simultaneous Positive-Unlabelled Learning and Conditional Generation with Extra Data. CoRR (2020)
Ju Xu, Mengzhang Li, Zhanxing Zhu
Automatic Data Augmentation for 3D Medical Image Segmentation. CoRR (2020)
Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective. CoRR (2020)
Lei Wu, Zhanxing Zhu
Towards Understanding and Improving the Transferability of Adversarial Examples in Deep Neural Networks. ACML (2020)
Hantao Guo, Rui Yan, Yansong Feng, Xuesong Gao, Zhanxing Zhu
Simplifying Graph Attention Networks with Source-Target Separation. ECAI (2020)
Quanming Yao, Ju Xu, Wei-Wei Tu, Zhanxing Zhu
Efficient Neural Architecture Search via Proximal Iterations. AAAI (2020)
Guixin Ye, Zhanyong Tang, Dingyi Fang, Zhanxing Zhu, Yansong Feng, Pengfei Xu, Xiaojiang Chen, Jungong Han, Zheng Wang
Using Generative Adversarial Networks to Break and Protect Text Captchas. ACM Trans. Priv. Secur. 23 (2) (2020)
Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. CoRR (2020)
Yanzhi Chen, Renjie Xie, Zhanxing Zhu
On Breaking Deep Generative Model-based Defenses and Beyond. ICML (2020)
Ju Xu, Mengzhang Li, Zhanxing Zhu
Automatic Data Augmentation for 3D Medical Image Segmentation. MICCAI (1) (2020)
Guangda Ji, Zhanxing Zhu
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher. CoRR (2020)
Guangda Ji, Zhanxing Zhu
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher. NeurIPS (2020)
Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective. ICML (2020)
Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. NeurIPS (2020)
Xuanyang Zhang, Hao Liu, Zhanxing Zhu, Zenglin Xu
Learning to Search Efficient DenseNet with Layer-wise Pruning. IJCNN (2020)
Weidi Sun, Yuteng Lu, Xiyue Zhang, Zhanxing Zhu, Meng Sun
Global Robustness Verification Networks. CoRR (2020)
Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu
Neural Approximate Sufficient Statistics for Implicit Models. CoRR (2020)
Ke Sun, Zhouchen Lin, Zhanxing Zhu
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes. AAAI (2020)
Haoyi Xiong, Kafeng Wang, Jiang Bian, Zhanxing Zhu, Cheng-Zhong Xu, Zhishan Guo, Jun Huan
SpHMC: Spectral Hamiltonian Monte Carlo. AAAI (2019)
Ke Sun, Zhanxing Zhu, Zhouchen Lin
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks. CoRR (2019)
Ke Sun, Zhanxing Zhu, Zhouchen Lin
Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors. CoRR (2019)
Bing Yu, Junzhao Zhang, Zhanxing Zhu
On the Learning Dynamics of Two-layer Nonlinear Convolutional Neural Networks. CoRR (2019)
Bing Yu, Jingfeng Wu, Jinwen Ma, Zhanxing Zhu
Tangent-Normal Adversarial Regularization for Semi-Supervised Learning. CVPR (2019)
Quanming Yao, Ju Xu, Wei-Wei Tu, Zhanxing Zhu
Differentiable Neural Architecture Search via Proximal Iterations. CoRR (2019)
Tianyuan Zhang, Zhanxing Zhu
Interpreting Adversarially Trained Convolutional Neural Networks. ICML (2019)
Ruosi Wan, Haoyi Xiong, Xingjian Li, Zhanxing Zhu, Jun Huan
Towards Making Deep Transfer Learning Never Hurt. CoRR (2019)
Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Zhanxing Zhu
The Multiplicative Noise in Stochastic Gradient Descent: Data-Dependent Regularization, Continuous and Discrete Approximation. CoRR (2019)
Tianyuan Zhang, Zhanxing Zhu
Interpreting Adversarially Trained Convolutional Neural Networks. CoRR (2019)
Ke Sun, Bing Yu, Zhouchen Lin, Zhanxing Zhu
Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy. CoRR (2019)
He Wang, Edmond S. L. Ho, Hubert P. H. Shum, Zhanxing Zhu
Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling. CoRR (2019)
Ke Sun, Hantao Guo, Zhanxing Zhu, Zhouchen Lin
Virtual Adversarial Training on Graph Convolutional Networks in Node Classification. CoRR (2019)
Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle. NeurIPS (2019)
Bing Yu, Mengzhang Li, Jiyong Zhang, Zhanxing Zhu
3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting. CoRR (2019)
Wenqing Hu, Zhanxing Zhu, Haoyi Xiong, Jun Huan
Quasi-potential as an implicit regularizer for the loss function in the stochastic gradient descent. CoRR (2019)
Ju Xu, Jin Ma, Zhanxing Zhu
Bayesian Optimized Continual Learning with Attention Mechanism. CoRR (2019)
Bing Yu, Haoteng Yin, Zhanxing Zhu
ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling. CoRR (2019)
Sen Hu, Lei Zou, Zhanxing Zhu
How Question Generation Can Help Question Answering over Knowledge Base. NLPCC (1) (2019)
Ke Sun, Zhouchen Lin, Hantao Guo, Zhanxing Zhu
Virtual Adversarial Training on Graph Convolutional Networks in Node Classification. PRCV (1) (2019)
Ke Sun, Zhouchen Lin, Zhanxing Zhu
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models. CoRR (2019)
Ruosi Wan, Mingjun Zhong, Haoyi Xiong, Zhanxing Zhu
Neural Control Variates for Monte Carlo Variance Reduction. ECML/PKDD (2) (2019)
Ruosi Wan, Haoyi Xiong, Xingjian Li, Zhanxing Zhu, Jun Huan
Towards Making Deep Transfer Learning Never Hurt. ICDM (2019)
Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects. ICML (2019)
Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle. CoRR (2019)
Ke Sun, Zhanxing Zhu, Zhouchen Lin
Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN. CoRR (2019)
Zhanxing Zhu, Ruosi Wan, Mingjun Zhong
Neural Control Variates for Variance Reduction. CoRR (2018)
Bing Yu, Jingfeng Wu, Zhanxing Zhu
Tangent-Normal Adversarial Regularization for Semi-supervised Learning. CoRR (2018)
Guixin Ye, Zhanyong Tang, Dingyi Fang, Zhanxing Zhu, Yansong Feng, Pengfei Xu, Xiaojiang Chen, Zheng Wang
Yet Another Text Captcha Solver: A Generative Adversarial Network Based Approach. CCS (2018)
Ju Xu, Zhanxing Zhu
Reinforced Continual Learning. NeurIPS (2018)
Nanyang Ye, Zhanxing Zhu
Bayesian Adversarial Learning. NeurIPS (2018)
Ju Xu, Zhanxing Zhu
Reinforced Continual Learning. CoRR (2018)
Nanyang Ye, Zhanxing Zhu
Stochastic Fractional Hamiltonian Monte Carlo. IJCAI (2018)
Lei Wu, Zhanxing Zhu, Cheng Tai, Weinan E
Understanding and Enhancing the Transferability of Adversarial Examples. CoRR (2018)
Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma
The Regularization Effects of Anisotropic Noise in Stochastic Gradient Descent. CoRR (2018)
Rui Luo, Jianhong Wang, Yaodong Yang, Jun Wang, Zhanxing Zhu
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning. NeurIPS (2018)
Bing Yu, Haoteng Yin, Zhanxing Zhu
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. IJCAI (2018)
Huizhuo Yuan, Jinzhu Jia, Zhanxing Zhu
SIPID: A deep learning framework for sinogram interpolation and image denoising in low-dose CT reconstruction. ISBI (2018)
Bingfeng Luo, Yansong Feng, Zheng Wang, Zhanxing Zhu, Songfang Huang, Rui Yan, Dongyan Zhao
Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix. CoRR (2017)
Nanyang Ye, Zhanxing Zhu, Rafal K. Mantiuk
Langevin Dynamics with Continuous Tempering for High-dimensional Non-convex Optimization. CoRR (2017)
Bingfeng Luo, Yansong Feng, Zheng Wang, Zhanxing Zhu, Songfang Huang, Rui Yan, Dongyan Zhao
Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix. ACL (1) (2017)
Cheng Feng, Tingting Li, Zhanxing Zhu, Deeph Chana
A Deep Learning-based Framework for Conducting Stealthy Attacks in Industrial Control Systems. CoRR (2017)
Bing Yu, Haoteng Yin, Zhanxing Zhu
Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting. CoRR (2017)
Nanyang Ye, Zhanxing Zhu, Rafal Mantiuk
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks. NIPS (2017)
Lei Wu, Zhanxing Zhu, Weinan E
Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes. CoRR (2017)
Zhanxing Zhu, Amos J. Storkey
Stochastic Parallel Block Coordinate Descent for Large-Scale Saddle Point Problems. AAAI (2016)
Zhanxing Zhu, Amos J. Storkey
Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle Point Problems. CoRR (2015)
Amos J. Storkey, Zhanxing Zhu, Jinli Hu
Aggregation Under Bias: Rényi Divergence Aggregation and Its Implementation via Machine Learning Markets. ECML/PKDD (1) (2015)
Xiaocheng Shang, Zhanxing Zhu, Benedict J. Leimkuhler, Amos J. Storkey
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling. CoRR (2015)
Zhanxing Zhu, Amos J. Storkey
Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle Point Problems. ECML/PKDD (1) (2015)
Xiaocheng Shang, Zhanxing Zhu, Benedict J. Leimkuhler, Amos J. Storkey
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling. NIPS (2015)
Zhanxing Zhu, Zhirong Yang, Erkki Oja
Multiplicative Updates for Learning with Stochastic Matrices. SCIA (2013)

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Publication Activity (10 Years)

Top Topics

Top Venues

Zhanxing Zhu