Zhanxing Zhu

According to our database, Zhanxing Zhu authored at least 88 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Stochastic gradient descent with random label noises: doubly stochastic models and inference stabilizer.

Haoyi Xiong, Xuhong Li, Boyang Yu, Dongrui Wu, Zhanxing Zhu, Dejing Dou

Mach. Learn. Sci. Technol., March, 2024

Memory-Efficient Gradient Unrolling for Large-Scale Bi-level Optimization.

Qianli Shen, Yezhen Wang, Zhouhao Yang, Xiang Li, Haonan Wang, Yang Zhang, Jonathan Scarlett, Zhanxing Zhu, Kenji Kawaguchi

CoRR, 2024

2023

An Annealing Mechanism for Adversarial Training Acceleration.

Nanyang Ye, Qianxiao Li, Xiao-Yun Zhou, Zhanxing Zhu

IEEE Trans. Neural Networks Learn. Syst., February, 2023

Doubly Stochastic Models: Learning with Unbiased Label Noises and Inference Stability.

Haoyi Xiong, Xuhong Li, Boyang Yu, Zhanxing Zhu, Dongrui Wu, Dejing Dou

CoRR, 2023

MonoFlow: Rethinking Divergence GANs via the Perspective of Differential Equations.

Mingxuan Yi, Zhanxing Zhu, Song Liu

CoRR, 2023

Implicit Bias of (Stochastic) Gradient Descent for Rank-1 Linear Neural Network.

Bochen Lyu, Zhanxing Zhu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Neural Lad: A Neural Latent Dynamics Framework for Times Series Modeling.

Ting Li, Jianguo Li, Zhanxing Zhu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows.

Mingxuan Yi, Zhanxing Zhu, Song Liu

Proceedings of the International Conference on Machine Learning, 2023

Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy.

Ke Sun, Bing Yu, Zhouchen Lin, Zhanxing Zhu

Proceedings of the Asian Conference on Machine Learning, 2023

Introduction to Data Science

Gaoyan Ou, Zhanxing Zhu, Bin Dong, Weinan E, Binyang Li, Shumin Shi

WorldScientific, ISBN: 9789811263910, 2023

2022

GrOD: Deep Learning with Gradients Orthogonal Decomposition for Knowledge Transfer, Distillation, and Adversarial Training.

Haoyi Xiong, Ruosi Wan, Jian Zhao, Zeyu Chen, Xingjian Li, Zhanxing Zhu, Jun Huan

ACM Trans. Knowl. Discov. Data, 2022

Adaptive Progressive Continual Learning.

Ju Xu, Jin Ma, Xuesong Gao, Zhanxing Zhu

IEEE Trans. Pattern Anal. Mach. Intell., 2022

Implicit Bias of Adversarial Training for Deep Neural Networks.

Bochen Lv, Zhanxing Zhu

Proceedings of the Tenth International Conference on Learning Representations, 2022

Fine-grained Differentiable Physics: A Yarn-level Model for Fabrics.

Deshan Gong, Zhanxing Zhu, Andrew J. Bulpitt, He Wang

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Spatio-Temporal Manifold Learning for Human Motions via Long-Horizon Modeling.

He Wang, Edmond S. L. Ho, Hubert P. H. Shum, Zhanxing Zhu

IEEE Trans. Vis. Comput. Graph., 2021

Sampling Sparse Representations with Randomized Measurement Langevin Dynamics.

Kafeng Wang, Haoyi Xiong, Jiang Bian, Zhanxing Zhu, Qian Gao, Zhishan Guo, Cheng-Zhong Xu, Jun Huan, Dejing Dou

ACM Trans. Knowl. Discov. Data, 2021

Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI.

Quanshi Zhang, Tian Han, Lixin Fan, Zhanxing Zhu, Hang Su, Ying Nian Wu, Jie Ren, Hao Zhang

CoRR, 2021

Spherical Motion Dynamics: Learning Dynamics of Normalized Neural Network using SGD and Weight Decay.

Ruosi Wan, Zhanxing Zhu, Xiangyu Zhang, Jian Sun

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization.

Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama

Proceedings of the 38th International Conference on Machine Learning, 2021

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models.

Ke Sun, Zhanxing Zhu, Zhouchen Lin

Proceedings of the 9th International Conference on Learning Representations, 2021

Neural Approximate Sufficient Statistics for Implicit Models.

Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu

Proceedings of the 9th International Conference on Learning Representations, 2021

Adversarial Invariant Learning.

Nanyang Ye, Jingxuan Tang, Huayu Deng, Xiao-Yun Zhou, Qianxiao Li, Zhenguo Li, Guang-Zhong Yang, Zhanxing Zhu

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Amata: An Annealing Mechanism for Adversarial Training Acceleration.

Nanyang Ye, Qianxiao Li, Xiao-Yun Zhou, Zhanxing Zhu

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting.

Mengzhang Li, Zhanxing Zhu

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Using Generative Adversarial Networks to Break and Protect Text Captchas.

Guixin Ye, Zhanyong Tang, Dingyi Fang, Zhanxing Zhu, Yansong Feng, Pengfei Xu, Xiaojiang Chen, Jungong Han, Zheng Wang

ACM Trans. Priv. Secur., 2020

Adversarial attacks on Faster R-CNN object detector.

Yutong Wang, Kunfeng Wang, Zhanxing Zhu, Feiyue Wang

Neurocomputing, 2020

Spherical Motion Dynamics of Deep Neural Networks with Batch Normalization and Weight Decay.

Ruosi Wan, Zhanxing Zhu, Xiangyu Zhang, Jian Sun

CoRR, 2020

Classify and Generate Reciprocally: Simultaneous Positive-Unlabelled Learning and Conditional Generation with Extra Data.

Bing Yu, Ke Sun, He Wang, Zhouchen Lin, Zhanxing Zhu

CoRR, 2020

Global Robustness Verification Networks.

Weidi Sun, Yuteng Lu, Xiyue Zhang, Zhanxing Zhu, Meng Sun

CoRR, 2020

Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework.

Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher.

Guangda Ji, Zhanxing Zhu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Automatic Data Augmentation for 3D Medical Image Segmentation.

Ju Xu, Mengzhang Li, Zhanxing Zhu

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

Learning to Search Efficient DenseNet with Layer-wise Pruning.

Xuanyang Zhang, Hao Liu, Zhanxing Zhu, Zenglin Xu

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

On the Noisy Gradient Descent that Generalizes as SGD.

Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu

Proceedings of the 37th International Conference on Machine Learning, 2020

Informative Dropout for Robust Representation Learning: A Shape-bias Perspective.

Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang

Proceedings of the 37th International Conference on Machine Learning, 2020

On Breaking Deep Generative Model-based Defenses and Beyond.

Yanzhi Chen, Renjie Xie, Zhanxing Zhu

Proceedings of the 37th International Conference on Machine Learning, 2020

Simplifying Graph Attention Networks with Source-Target Separation.

Hantao Guo, Rui Yan, Yansong Feng, Xuesong Gao, Zhanxing Zhu

Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020

Towards Understanding and Improving the Transferability of Adversarial Examples in Deep Neural Networks.

Lei Wu, Zhanxing Zhu

Proceedings of The 12th Asian Conference on Machine Learning, 2020

Efficient Neural Architecture Search via Proximal Iterations.

Quanming Yao, Ju Xu, Wei-Wei Tu, Zhanxing Zhu

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes.

Ke Sun, Zhouchen Lin, Zhanxing Zhu

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

The Multiplicative Noise in Stochastic Gradient Descent: Data-Dependent Regularization, Continuous and Discrete Approximation.

Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Zhanxing Zhu

CoRR, 2019

Differentiable Neural Architecture Search via Proximal Iterations.

Quanming Yao, Ju Xu, Wei-Wei Tu, Zhanxing Zhu

CoRR, 2019

On the Learning Dynamics of Two-layer Nonlinear Convolutional Neural Networks.

Bing Yu, Junzhao Zhang, Zhanxing Zhu

CoRR, 2019

Bayesian Optimized Continual Learning with Attention Mechanism.

Ju Xu, Jin Ma, Zhanxing Zhu

CoRR, 2019

ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling.

Bing Yu, Haoteng Yin, Zhanxing Zhu

CoRR, 2019

3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting.

Bing Yu, Mengzhang Li, Jiyong Zhang, Zhanxing Zhu

CoRR, 2019

Multi-Stage Self-Supervised Learning for Graph Convolutional Networks.

Ke Sun, Zhanxing Zhu, Zhouchen Lin

CoRR, 2019

Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN.

Ke Sun, Zhanxing Zhu, Zhouchen Lin

CoRR, 2019

Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors.

Ke Sun, Zhanxing Zhu, Zhouchen Lin

CoRR, 2019

Quasi-potential as an implicit regularizer for the loss function in the stochastic gradient descent.

Wenqing Hu, Zhanxing Zhu, Haoyi Xiong, Jun Huan

CoRR, 2019

Virtual Adversarial Training on Graph Convolutional Networks in Node Classification.

Ke Sun, Zhouchen Lin, Hantao Guo, Zhanxing Zhu

Proceedings of the Pattern Recognition and Computer Vision - Second Chinese Conference, 2019

Neural Control Variates for Monte Carlo Variance Reduction.

Ruosi Wan, Mingjun Zhong, Haoyi Xiong, Zhanxing Zhu

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

How Question Generation Can Help Question Answering over Knowledge Base.

Sen Hu, Lei Zou, Zhanxing Zhu

Proceedings of the Natural Language Processing and Chinese Computing, 2019

You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle.

Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects.

Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma

Proceedings of the 36th International Conference on Machine Learning, 2019

Interpreting Adversarially Trained Convolutional Neural Networks.

Tianyuan Zhang, Zhanxing Zhu

Proceedings of the 36th International Conference on Machine Learning, 2019

Towards Making Deep Transfer Learning Never Hurt.

Ruosi Wan, Haoyi Xiong, Xingjian Li, Zhanxing Zhu, Jun Huan

Proceedings of the 2019 IEEE International Conference on Data Mining, 2019

Tangent-Normal Adversarial Regularization for Semi-Supervised Learning.

Bing Yu, Jingfeng Wu, Jinwen Ma, Zhanxing Zhu

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

SpHMC: Spectral Hamiltonian Monte Carlo.

Haoyi Xiong, Kafeng Wang, Jiang Bian, Zhanxing Zhu, Cheng-Zhong Xu, Zhishan Guo, Jun Huan

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Tangent-Normal Adversarial Regularization for Semi-supervised Learning.

Bing Yu, Jingfeng Wu, Zhanxing Zhu

CoRR, 2018

Neural Control Variates for Variance Reduction.

Zhanxing Zhu, Ruosi Wan, Mingjun Zhong

CoRR, 2018

The Regularization Effects of Anisotropic Noise in Stochastic Gradient Descent.

Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma

CoRR, 2018

Understanding and Enhancing the Transferability of Adversarial Examples.

Lei Wu, Zhanxing Zhu, Cheng Tai, Weinan E

CoRR, 2018

Bayesian Adversarial Learning.

Nanyang Ye, Zhanxing Zhu

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Reinforced Continual Learning.

Ju Xu, Zhanxing Zhu

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning.

Rui Luo, Jianhong Wang, Yaodong Yang, Jun Wang, Zhanxing Zhu

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

SIPID: A deep learning framework for sinogram interpolation and image denoising in low-dose CT reconstruction.

Huizhuo Yuan, Jinzhu Jia, Zhanxing Zhu

Proceedings of the 15th IEEE International Symposium on Biomedical Imaging, 2018

Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting.

Bing Yu, Haoteng Yin, Zhanxing Zhu

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Stochastic Fractional Hamiltonian Monte Carlo.

Nanyang Ye, Zhanxing Zhu

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Yet Another Text Captcha Solver: A Generative Adversarial Network Based Approach.

Guixin Ye, Zhanyong Tang, Dingyi Fang, Zhanxing Zhu, Yansong Feng, Pengfei Xu, Xiaojiang Chen, Zheng Wang

Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, 2018

2017

A Deep Learning-based Framework for Conducting Stealthy Attacks in Industrial Control Systems.

Cheng Feng, Tingting Li, Zhanxing Zhu, Deeph Chana

CoRR, 2017

Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting.

Bing Yu, Haoteng Yin, Zhanxing Zhu

CoRR, 2017

Langevin Dynamics with Continuous Tempering for High-dimensional Non-convex Optimization.

Nanyang Ye, Zhanxing Zhu, Rafal K. Mantiuk

CoRR, 2017

Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes.

Lei Wu, Zhanxing Zhu, Weinan E

CoRR, 2017

Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks.

Nanyang Ye, Zhanxing Zhu, Rafal Mantiuk

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix.

Bingfeng Luo, Yansong Feng, Zheng Wang, Zhanxing Zhu, Songfang Huang, Rui Yan, Dongyan Zhao

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017

2016

Integrating local information for inference and optimization in machine learning.

Zhanxing Zhu

PhD thesis, 2016

Stochastic Parallel Block Coordinate Descent for Large-Scale Saddle Point Problems.

Zhanxing Zhu, Amos J. Storkey

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle Point Problems.

Zhanxing Zhu, Amos J. Storkey

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Aggregation Under Bias: Rényi Divergence Aggregation and Its Implementation via Machine Learning Markets.

Amos J. Storkey, Zhanxing Zhu, Jinli Hu

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling.

Xiaocheng Shang, Zhanxing Zhu, Benedict J. Leimkuhler, Amos J. Storkey

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2013

Supervised Distance Preserving Projections.

Zhanxing Zhu, Timo Similä, Francesco Corona

Neural Process. Lett., 2013

Multiplicative Updates for Learning with Stochastic Matrices.

Zhanxing Zhu, Zhirong Yang, Erkki Oja

Proceedings of the Image Analysis, 18th Scandinavian Conference, 2013

2011

Hyperspectral unmixing using non-negative matrix factorization with automatically estimating regularization parameters.

Zhenwei Shi, Zhenyu An, Xueyan Tan, Zhanxing Zhu, Zhiguo Jiang

Proceedings of the Seventh International Conference on Natural Computation, 2011

2010

A method of automatically estimating the regularization parameter for Non-negative Matrix Factorization.

Dalong Cheng, Zhenwei Shi, Xueyan Tan, Zhanxing Zhu, Zhiguo Jiang

Proceedings of the Sixth International Conference on Natural Computation, 2010

Automatic Rank Determination in Projective Nonnegative Matrix Factorization.

Zhirong Yang, Zhanxing Zhu, Erkki Oja

Proceedings of the Latent Variable Analysis and Signal Separation, 2010

2009

Quadratic Form Innovation to Blind Source Separation.

Zhenwei Shi, Zhanxing Zhu, Xueyan Tan, Zhiguo Jiang

Proceedings of the Fifth International Conference on Natural Computation, 2009

A Fixed-Point Algorithm for Nonnegative Independent Component Analysis.

Zhenwei Shi, Xueyan Tan, Zhanxing Zhu, Zhiguo Jiang

Proceedings of the Fifth International Conference on Natural Computation, 2009