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2020 – today
- 2024
[j9]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): 15039 (2024)- [i45]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 abs/2406.14095 (2024) - 2023
- [b2]Gaoyan Ou, Zhanxing Zhu, Bin Dong, Weinan E, Binyang Li, Shumin Shi:
Introduction to Data Science. WorldScientific 2023, ISBN 9789811263897, pp. 1-444 - [j8]Nanyang Ye, Qianxiao Li, Xiao-Yun Zhou, Zhanxing Zhu:
An Annealing Mechanism for Adversarial Training Acceleration. IEEE Trans. Neural Networks Learn. Syst. 34(2): 882-893 (2023) - [c52]Ke Sun, Bing Yu, Zhouchen Lin, Zhanxing Zhu:
Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy. ACML 2023: 1276-1291 - [c51]Mingxuan Yi, Zhanxing Zhu, Song Liu:
MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows. ICML 2023: 39984-40000 - [c50]Ting Li, Jianguo Li, Zhanxing Zhu:
Neural Lad: A Neural Latent Dynamics Framework for Times Series Modeling. NeurIPS 2023 - [c49]Bochen Lyu, Zhanxing Zhu:
Implicit Bias of (Stochastic) Gradient Descent for Rank-1 Linear Neural Network. NeurIPS 2023 - [i44]Mingxuan Yi, Zhanxing Zhu, Song Liu:
MonoFlow: Rethinking Divergence GANs via the Perspective of Differential Equations. CoRR abs/2302.01075 (2023) - [i43]Haoyi Xiong, Xuhong Li, Boyang Yu, Zhanxing Zhu, Dongrui Wu, Dejing Dou:
Doubly Stochastic Models: Learning with Unbiased Label Noises and Inference Stability. CoRR abs/2304.00320 (2023) - 2022
- [j7]Ju Xu, Jin Ma, Xuesong Gao, Zhanxing Zhu:
Adaptive Progressive Continual Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(10): 6715-6728 (2022) - [j6]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): 117:1-117:25 (2022) - [c48]Deshan Gong, Zhanxing Zhu, Andrew J. Bulpitt, He Wang:
Fine-grained Differentiable Physics: A Yarn-level Model for Fabrics. ICLR 2022 - [c47]Bochen Lv, Zhanxing Zhu:
Implicit Bias of Adversarial Training for Deep Neural Networks. ICLR 2022 - [i42]Deshan Gong, Zhanxing Zhu, Andrew J. Bulpitt, He Wang:
Fine-grained differentiable physics: a yarn-level model for fabrics. CoRR abs/2202.00504 (2022) - 2021
- [j5]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): 21:1-21:21 (2021) - [j4]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): 216-227 (2021) - [c46]Mengzhang Li, Zhanxing Zhu:
Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting. AAAI 2021: 4189-4196 - [c45]Nanyang Ye, Qianxiao Li, Xiao-Yun Zhou, Zhanxing Zhu:
Amata: An Annealing Mechanism for Adversarial Training Acceleration. AAAI 2021: 10691-10699 - [c44]Nanyang Ye, Jingxuan Tang, Huayu Deng, Xiao-Yun Zhou, Qianxiao Li, Zhenguo Li, Guang-Zhong Yang, Zhanxing Zhu:
Adversarial Invariant Learning. CVPR 2021: 12446-12454 - [c43]Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu:
Neural Approximate Sufficient Statistics for Implicit Models. ICLR 2021 - [c42]Ke Sun, Zhanxing Zhu, Zhouchen Lin:
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models. ICLR 2021 - [c41]Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama:
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization. ICML 2021: 11448-11458 - [c40]Ruosi Wan, Zhanxing Zhu, Xiangyu Zhang, Jian Sun:
Spherical Motion Dynamics: Learning Dynamics of Normalized Neural Network using SGD and Weight Decay. NeurIPS 2021: 6380-6391 - [i41]Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama:
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization. CoRR abs/2103.17182 (2021) - [i40]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 abs/2107.08821 (2021) - 2020
- [j3]Yutong Wang, Kunfeng Wang, Zhanxing Zhu, Feiyue Wang:
Adversarial attacks on Faster R-CNN object detector. Neurocomputing 382: 87-95 (2020) - [j2]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): 7:1-7:29 (2020) - [c39]Ke Sun, Zhouchen Lin, Zhanxing Zhu:
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes. AAAI 2020: 5892-5899 - [c38]Quanming Yao, Ju Xu, Wei-Wei Tu, Zhanxing Zhu:
Efficient Neural Architecture Search via Proximal Iterations. AAAI 2020: 6664-6671 - [c37]Lei Wu, Zhanxing Zhu:
Towards Understanding and Improving the Transferability of Adversarial Examples in Deep Neural Networks. ACML 2020: 837-850 - [c36]Hantao Guo, Rui Yan, Yansong Feng, Xuesong Gao, Zhanxing Zhu:
Simplifying Graph Attention Networks with Source-Target Separation. ECAI 2020: 1166-1173 - [c35]Yanzhi Chen, Renjie Xie, Zhanxing Zhu:
On Breaking Deep Generative Model-based Defenses and Beyond. ICML 2020: 1736-1745 - [c34]Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang:
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective. ICML 2020: 8828-8839 - [c33]Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu:
On the Noisy Gradient Descent that Generalizes as SGD. ICML 2020: 10367-10376 - [c32]Xuanyang Zhang, Hao Liu, Zhanxing Zhu, Zenglin Xu:
Learning to Search Efficient DenseNet with Layer-wise Pruning. IJCNN 2020: 1-8 - [c31]Ju Xu, Mengzhang Li, Zhanxing Zhu:
Automatic Data Augmentation for 3D Medical Image Segmentation. MICCAI (1) 2020: 378-387 - [c30]Guangda Ji, Zhanxing Zhu:
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher. NeurIPS 2020 - [c29]Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu:
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. NeurIPS 2020 - [i39]Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu:
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. CoRR abs/2002.09169 (2020) - [i38]Weidi Sun, Yuteng Lu, Xiyue Zhang, Zhanxing Zhu, Meng Sun:
Global Robustness Verification Networks. CoRR abs/2006.04403 (2020) - [i37]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 abs/2006.07841 (2020) - [i36]Ruosi Wan, Zhanxing Zhu, Xiangyu Zhang, Jian Sun:
Spherical Motion Dynamics of Deep Neural Networks with Batch Normalization and Weight Decay. CoRR abs/2006.08419 (2020) - [i35]Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang:
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective. CoRR abs/2008.04254 (2020) - [i34]Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu:
Neural Approximate Sufficient Statistics for Implicit Models. CoRR abs/2010.10079 (2020) - [i33]Guangda Ji, Zhanxing Zhu:
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher. CoRR abs/2010.10090 (2020) - [i32]Ju Xu, Mengzhang Li, Zhanxing Zhu:
Automatic Data Augmentation for 3D Medical Image Segmentation. CoRR abs/2010.11695 (2020) - [i31]Nanyang Ye, Qianxiao Li, Xiao-Yun Zhou, Zhanxing Zhu:
Amata: An Annealing Mechanism for Adversarial Training Acceleration. CoRR abs/2012.08112 (2020) - [i30]Mengzhang Li, Zhanxing Zhu:
Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting. CoRR abs/2012.09641 (2020)
2010 – 2019
- 2019
- [c28]Haoyi Xiong, Kafeng Wang, Jiang Bian, Zhanxing Zhu, Cheng-Zhong Xu, Zhishan Guo, Jun Huan:
SpHMC: Spectral Hamiltonian Monte Carlo. AAAI 2019: 5516-5524 - [c27]Bing Yu, Jingfeng Wu, Jinwen Ma, Zhanxing Zhu:
Tangent-Normal Adversarial Regularization for Semi-Supervised Learning. CVPR 2019: 10676-10684 - [c26]Ruosi Wan, Haoyi Xiong, Xingjian Li, Zhanxing Zhu, Jun Huan:
Towards Making Deep Transfer Learning Never Hurt. ICDM 2019: 578-587 - [c25]Tianyuan Zhang, Zhanxing Zhu:
Interpreting Adversarially Trained Convolutional Neural Networks. ICML 2019: 7502-7511 - [c24]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: 7654-7663 - [c23]Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong:
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle. NeurIPS 2019: 227-238 - [c22]Sen Hu, Lei Zou, Zhanxing Zhu:
How Question Generation Can Help Question Answering over Knowledge Base. NLPCC (1) 2019: 80-92 - [c21]Ruosi Wan, Mingjun Zhong, Haoyi Xiong, Zhanxing Zhu:
Neural Control Variates for Monte Carlo Variance Reduction. ECML/PKDD (2) 2019: 533-547 - [c20]Ke Sun, Zhouchen Lin, Hantao Guo, Zhanxing Zhu:
Virtual Adversarial Training on Graph Convolutional Networks in Node Classification. PRCV (1) 2019: 431-443 - [i29]Wenqing Hu, Zhanxing Zhu, Haoyi Xiong, Jun Huan:
Quasi-potential as an implicit regularizer for the loss function in the stochastic gradient descent. CoRR abs/1901.06054 (2019) - [i28]Ke Sun, Zhanxing Zhu, Zhouchen Lin:
Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors. CoRR abs/1902.11019 (2019) - [i27]Ke Sun, Zhanxing Zhu, Zhouchen Lin:
Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN. CoRR abs/1902.11029 (2019) - [i26]Ke Sun, Zhanxing Zhu, Zhouchen Lin:
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks. CoRR abs/1902.11038 (2019) - [i25]Ke Sun, Hantao Guo, Zhanxing Zhu, Zhouchen Lin:
Virtual Adversarial Training on Graph Convolutional Networks in Node Classification. CoRR abs/1902.11045 (2019) - [i24]Bing Yu, Mengzhang Li, Jiyong Zhang, Zhanxing Zhu:
3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting. CoRR abs/1903.00919 (2019) - [i23]Bing Yu, Haoteng Yin, Zhanxing Zhu:
ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling. CoRR abs/1903.05631 (2019) - [i22]Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong:
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle. CoRR abs/1905.00877 (2019) - [i21]Ju Xu, Jin Ma, Zhanxing Zhu:
Bayesian Optimized Continual Learning with Attention Mechanism. CoRR abs/1905.03980 (2019) - [i20]Tianyuan Zhang, Zhanxing Zhu:
Interpreting Adversarially Trained Convolutional Neural Networks. CoRR abs/1905.09797 (2019) - [i19]Bing Yu, Junzhao Zhang, Zhanxing Zhu:
On the Learning Dynamics of Two-layer Nonlinear Convolutional Neural Networks. CoRR abs/1905.10157 (2019) - [i18]Quanming Yao, Ju Xu, Wei-Wei Tu, Zhanxing Zhu:
Differentiable Neural Architecture Search via Proximal Iterations. CoRR abs/1905.13577 (2019) - [i17]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 abs/1906.07405 (2019) - [i16]Ke Sun, Zhouchen Lin, Zhanxing Zhu:
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models. CoRR abs/1908.05081 (2019) - [i15]He Wang, Edmond S. L. Ho, Hubert P. H. Shum, Zhanxing Zhu:
Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling. CoRR abs/1908.07214 (2019) - [i14]Ruosi Wan, Haoyi Xiong, Xingjian Li, Zhanxing Zhu, Jun Huan:
Towards Making Deep Transfer Learning Never Hurt. CoRR abs/1911.07489 (2019) - [i13]Ke Sun, Bing Yu, Zhouchen Lin, Zhanxing Zhu:
Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy. CoRR abs/1911.09307 (2019) - 2018
- [c19]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: 332-348 - [c18]Nanyang Ye, Zhanxing Zhu:
Stochastic Fractional Hamiltonian Monte Carlo. IJCAI 2018: 3019-3025 - [c17]Bing Yu, Haoteng Yin, Zhanxing Zhu:
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. IJCAI 2018: 3634-3640 - [c16]Huizhuo Yuan, Jinzhu Jia, Zhanxing Zhu:
SIPID: A deep learning framework for sinogram interpolation and image denoising in low-dose CT reconstruction. ISBI 2018: 1521-1524 - [c15]Ju Xu, Zhanxing Zhu:
Reinforced Continual Learning. NeurIPS 2018: 907-916 - [c14]Nanyang Ye, Zhanxing Zhu:
Bayesian Adversarial Learning. NeurIPS 2018: 6892-6901 - [c13]Rui Luo, Jianhong Wang, Yaodong Yang, Jun Wang, Zhanxing Zhu:
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning. NeurIPS 2018: 10696-10705 - [i12]Lei Wu, Zhanxing Zhu, Cheng Tai, Weinan E:
Understanding and Enhancing the Transferability of Adversarial Examples. CoRR abs/1802.09707 (2018) - [i11]Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma:
The Regularization Effects of Anisotropic Noise in Stochastic Gradient Descent. CoRR abs/1803.00195 (2018) - [i10]Ju Xu, Zhanxing Zhu:
Reinforced Continual Learning. CoRR abs/1805.12369 (2018) - [i9]Zhanxing Zhu, Ruosi Wan, Mingjun Zhong:
Neural Control Variates for Variance Reduction. CoRR abs/1806.00159 (2018) - [i8]Bing Yu, Jingfeng Wu, Zhanxing Zhu:
Tangent-Normal Adversarial Regularization for Semi-supervised Learning. CoRR abs/1808.06088 (2018) - 2017
- [c12]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: 430-439 - [c11]Nanyang Ye, Zhanxing Zhu, Rafal Mantiuk:
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks. NIPS 2017: 618-626 - [i7]Nanyang Ye, Zhanxing Zhu, Rafal K. Mantiuk:
Langevin Dynamics with Continuous Tempering for High-dimensional Non-convex Optimization. CoRR abs/1703.04379 (2017) - [i6]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 abs/1705.03995 (2017) - [i5]Lei Wu, Zhanxing Zhu, Weinan E:
Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes. CoRR abs/1706.10239 (2017) - [i4]Bing Yu, Haoteng Yin, Zhanxing Zhu:
Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting. CoRR abs/1709.04875 (2017) - [i3]Cheng Feng, Tingting Li, Zhanxing Zhu, Deeph Chana:
A Deep Learning-based Framework for Conducting Stealthy Attacks in Industrial Control Systems. CoRR abs/1709.06397 (2017) - 2016
- [b1]Zhanxing Zhu:
Integrating local information for inference and optimization in machine learning. University of Edinburgh, UK, 2016 - [c10]Zhanxing Zhu, Amos J. Storkey:
Stochastic Parallel Block Coordinate Descent for Large-Scale Saddle Point Problems. AAAI 2016: 2429-2437 - 2015
- [c9]Xiaocheng Shang, Zhanxing Zhu, Benedict J. Leimkuhler, Amos J. Storkey:
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling. NIPS 2015: 37-45 - [c8]Amos J. Storkey, Zhanxing Zhu, Jinli Hu:
Aggregation Under Bias: Rényi Divergence Aggregation and Its Implementation via Machine Learning Markets. ECML/PKDD (1) 2015: 560-574 - [c7]Zhanxing Zhu, Amos J. Storkey:
Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle Point Problems. ECML/PKDD (1) 2015: 645-658 - [i2]Zhanxing Zhu, Amos J. Storkey:
Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle Point Problems. CoRR abs/1506.04093 (2015) - [i1]Xiaocheng Shang, Zhanxing Zhu, Benedict J. Leimkuhler, Amos J. Storkey:
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling. CoRR abs/1510.08692 (2015) - 2013
- [j1]Zhanxing Zhu, Timo Similä, Francesco Corona:
Supervised Distance Preserving Projections. Neural Process. Lett. 38(3): 445-463 (2013) - [c6]Zhanxing Zhu, Zhirong Yang, Erkki Oja:
Multiplicative Updates for Learning with Stochastic Matrices. SCIA 2013: 143-152 - 2011
- [c5]Zhenwei Shi, Zhenyu An, Xueyan Tan, Zhanxing Zhu, Zhiguo Jiang:
Hyperspectral unmixing using non-negative matrix factorization with automatically estimating regularization parameters. ICNC 2011: 1836-1840 - 2010
- [c4]Zhirong Yang, Zhanxing Zhu, Erkki Oja:
Automatic Rank Determination in Projective Nonnegative Matrix Factorization. LVA/ICA 2010: 514-521 - [c3]Dalong Cheng, Zhenwei Shi, Xueyan Tan, Zhanxing Zhu, Zhiguo Jiang:
A method of automatically estimating the regularization parameter for Non-negative Matrix Factorization. ICNC 2010: 22-26
2000 – 2009
- 2009
- [c2]Zhenwei Shi, Xueyan Tan, Zhanxing Zhu, Zhiguo Jiang:
A Fixed-Point Algorithm for Nonnegative Independent Component Analysis. ICNC (2) 2009: 482-485 - [c1]Zhenwei Shi, Zhanxing Zhu, Xueyan Tan, Zhiguo Jiang:
Quadratic Form Innovation to Blind Source Separation. ICNC (2) 2009: 594-597
Coauthor Index
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