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ORCIDJun Zhu 0001
Person information
- affiliation: Tsinghua University, State Key Laboratory of Intelligent Technology and Systems, TNList, Beijing, China
- affiliation: Tsinghua University, Department of Computer Science and Technology, Beijing, China
- affiliation (former): Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA, USA
Other persons with the same name
- Jun Zhu — disambiguation page
- Jun Zhu 0002 — University of North Carolina at Charlotte, Charlotte, NC, USA
- Jun Zhu 0003 — Technical University of Eindhoven, The Netherlands
- Jun Zhu 0004 — Nanjing Normal University, China
- Jun Zhu 0005
![[0000-0002-1773-548X]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-1773-548X")
— The University of British Columbia, Department of Electrical and Computer Engineering, Vancouver, BC, Canada - Jun Zhu 0006 — Xiamen University, Department of Chemistry, China (and 1 more)
- Jun Zhu 0007 — Southwest Jiaotong University, Faculty of Geosciences and Environmental Engineering, Chengdu, China
- Jun Zhu 0008 — Nanjing University of Information Science and Technology, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, China (and 2 more)
- Jun Zhu 0009 — University of Electronic Science and Technology of China, School of Electronic Engineering, Chengdu, China
- Jun Zhu 0010 — Jinling Institute of Technology, Nanjing, China (and 1 more)
- Jun Zhu 0011 — Royal Institute of Technology, Stockholm, Sweden
- Jun Zhu 0012 — Northwestern Polytechnical University, Department of Civil Aviation, Xi'an, China (and 1 more)
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Journal Articles
- 2024
[j60]Ke Su, Hang Su, Chongxuan Li, Jun Zhu, Bo Zhang:
Probabilistic Neural-Symbolic Models With Inductive Posterior Constraints. IEEE Trans. Neural Networks Learn. Syst. 35(2): 2667-2679 (2024)- 2023
- [j59]Zhijie Deng, Yinpeng Dong, Jun Zhu:
Batch virtual adversarial training for graph convolutional networks. AI Open 4: 73-79 (2023) - [j58]Bo Zhang, Jun Zhu, Hang Su:
Toward the third generation artificial intelligence. Sci. China Inf. Sci. 66(2) (2023) - [j57]Yichi Zhang, Zijian Zhu, Hang Su, Jun Zhu, Shibao Zheng, Yuan He, Hui Xue:
To make yourself invisible with Adversarial Semantic Contours. Comput. Vis. Image Underst. 230: 103659 (2023) - [j56]Feng Zhou, Quyu Kong, Zhijie Deng, Fengxiang He, Peng Cui, Jun Zhu:
Heterogeneous multi-task Gaussian Cox processes. Mach. Learn. 112(12): 5105-5134 (2023) - [j55]Yuhao Zhou, Chenglong Bao, Chao Ding, Jun Zhu:
A semismooth Newton based augmented Lagrangian method for nonsmooth optimization on matrix manifolds. Math. Program. 201(1): 1-61 (2023) - [j54]Yudeng Lin, Qingtian Zhang, Bin Gao, Jianshi Tang, Peng Yao, Chongxuan Li, Shiyu Huang, Zhengwu Liu, Ying Zhou, Yuyi Liu, Wenqiang Zhang, Jun Zhu, He Qian, Huaqiang Wu:
Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning. Nat. Mac. Intell. 5(7): 714-723 (2023) - [j53]Liyuan Wang, Xingxing Zhang, Qian Li, Mingtian Zhang, Hang Su, Jun Zhu, Yi Zhong:
Incorporating neuro-inspired adaptability for continual learning in artificial intelligence. Nat. Mac. Intell. 5(12): 1356-1368 (2023) - 2022
- [j52]Zhanhao Hu, Jun Zhu, Bo Zhang, Xiaolin Hu:
Amplification trojan network: Attack deep neural networks by amplifying their inherent weakness. Neurocomputing 505: 142-153 (2022) - [j51]Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Yi Su, Hang Su, Jun Zhu:
Tianshou: A Highly Modularized Deep Reinforcement Learning Library. J. Mach. Learn. Res. 23: 267:1-267:6 (2022) - [j50]Yinpeng Dong, Shuyu Cheng, Tianyu Pang, Hang Su, Jun Zhu:
Query-Efficient Black-Box Adversarial Attacks Guided by a Transfer-Based Prior. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 9536-9548 (2022) - [j49]Chongxuan Li, Kun Xu, Jun Zhu, Jiashuo Liu, Bo Zhang:
Triple Generative Adversarial Networks. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 9629-9640 (2022) - [j48]Hongyang Gu, Jianmin Li, Guangyuan Fu, Min Yue, Jun Zhu:
Loss function search for person re-identification. Pattern Recognit. 124: 108432 (2022) - [j47]Dong Yan, Jiayi Weng, Shiyu Huang, Chongxuan Li, Yichi Zhou, Hang Su, Jun Zhu:
Deep reinforcement learning with credit assignment for combinatorial optimization. Pattern Recognit. 124: 108466 (2022) - [j46]Xiao Yang, Shilong Liu, Yinpeng Dong, Hang Su, Lei Zhang, Jun Zhu:
Towards generalizable detection of face forgery via self-guided model-agnostic learning. Pattern Recognit. Lett. 160: 98-104 (2022) - [j45]Liyuan Wang, Bo Lei, Qian Li, Hang Su, Jun Zhu, Yi Zhong:
Triple-Memory Networks: A Brain-Inspired Method for Continual Learning. IEEE Trans. Neural Networks Learn. Syst. 33(5): 1925-1934 (2022) - [j44]Weikai Yang, Xi Ye, Xingxing Zhang, Lanxi Xiao, Jiazhi Xia, Zhongyuan Wang, Jun Zhu, Hanspeter Pfister, Shixia Liu:
Diagnosing Ensemble Few-Shot Classifiers. IEEE Trans. Vis. Comput. Graph. 28(9): 3292-3306 (2022) - 2021
- [j43]Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Yuan Yao, Ao Zhang, Liang Zhang, Wentao Han, Minlie Huang, Qin Jin, Yanyan Lan, Yang Liu, Zhiyuan Liu, Zhiwu Lu, Xipeng Qiu, Ruihua Song, Jie Tang, Ji-Rong Wen, Jinhui Yuan, Wayne Xin Zhao, Jun Zhu:
Pre-trained models: Past, present and future. AI Open 2: 225-250 (2021) - [j42]Hongyang Gu, Guangyuan Fu, Jianmin Li, Jun Zhu:
Auto-ReID+: Searching for a multi-branch ConvNet for person re-identification. Neurocomputing 435: 53-66 (2021) - [j41]Hongyang Gu, Guangyuan Fu, Xu Wang, Jun Zhu:
Learning auto-scale representations for person re-identification. Image Vis. Comput. 112: 104241 (2021) - [j40]Yueqiao Li, Hang Su, Jun Zhu:
AdvCapsNet: To defense adversarial attacks based on Capsule networks. J. Vis. Commun. Image Represent. 75: 103037 (2021) - [j39]Jialuo Liu, Tingjin Chu, Jun Zhu, Haonan Wang:
Semiparametric method and theory for continuously indexed spatio-temporal processes. J. Multivar. Anal. 183: 104735 (2021) - [j38]Kelei Cao, Mengchen Liu, Hang Su, Jing Wu, Jun Zhu, Shixia Liu:
Analyzing the Noise Robustness of Deep Neural Networks. IEEE Trans. Vis. Comput. Graph. 27(7): 3289-3304 (2021) - 2020
- [j37]Jian Wu, Xiaoguang Liu, Xiaolin Hu, Jun Zhu:
PopMNet: Generating structured pop music melodies using neural networks. Artif. Intell. 286: 103303 (2020) - [j36]Ke Su, Hang Su, Jianguo Li, Jun Zhu:
Toward Accurate Visual Reasoning With Dual-Path Neural Module Networks. Frontiers Robotics AI 7: 109 (2020) - [j35]Fei Wu, Cewu Lu, Mingjie Zhu, Hao Chen, Jun Zhu, Kai Yu, Lei Li, Ming Li, Qianfeng Chen, Xi Li, Xudong Cao, Zhongyuan Wang, Zhengjun Zha, Yueting Zhuang, Yunhe Pan:
Towards a new generation of artificial intelligence in China. Nat. Mach. Intell. 2(6): 312-316 (2020) - [j34]Jian Wu, Changran Hu, Yulong Wang, Xiaolin Hu, Jun Zhu:
A Hierarchical Recurrent Neural Network for Symbolic Melody Generation. IEEE Trans. Cybern. 50(6): 2749-2757 (2020) - [j33]Kaiwei Li, Jianfei Chen, Wenguang Chen, Jun Zhu:
SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs. IEEE Trans. Parallel Distributed Syst. 31(9): 2112-2124 (2020) - 2019
- [j32]Yinpeng Dong, Renkun Ni, Jianguo Li, Yurong Chen, Hang Su, Jun Zhu:
Stochastic Quantization for Learning Accurate Low-Bit Deep Neural Networks. Int. J. Comput. Vis. 127(11-12): 1629-1642 (2019) - [j31]Nikolai Zakharov, Hang Su, Jun Zhu, Jan Gläscher:
Towards controllable image descriptions with semi-supervised VAE. J. Vis. Commun. Image Represent. 63 (2019) - [j30]Zhize Li, Tianyi Zhang, Shuyu Cheng, Jun Zhu, Jian Li:
Stochastic gradient Hamiltonian Monte Carlo with variance reduction for Bayesian inference. Mach. Learn. 108(8-9): 1701-1727 (2019) - [j29]Tian Tian, Jun Zhu, You Qiaoben:
Max-Margin Majority Voting for Learning from Crowds. IEEE Trans. Pattern Anal. Mach. Intell. 41(10): 2480-2494 (2019) - 2018
- [j28]Ning Chen, Jun Zhu, Jianfei Chen, Ting Chen:
Dropout training for SVMs with data augmentation. Frontiers Comput. Sci. 12(4): 694-713 (2018) - [j27]Yong Ren, Yining Wang, Jun Zhu:
Spectral Learning for Supervised Topic Models. IEEE Trans. Pattern Anal. Mach. Intell. 40(3): 726-739 (2018) - [j26]Chongxuan Li, Jun Zhu, Bo Zhang:
Max-Margin Deep Generative Models for (Semi-)Supervised Learning. IEEE Trans. Pattern Anal. Mach. Intell. 40(11): 2762-2775 (2018) - [j25]Mowei Wang, Yong Cui, Shihan Xiao, Xin Wang, Dan Yang, Kai Chen, Jun Zhu:
Neural Network Meets DCN: Traffic-driven Topology Adaptation with Deep Learning. Proc. ACM Meas. Anal. Comput. Syst. 2(2): 26:1-26:25 (2018) - [j24]Jianfei Chen, Jun Zhu, Jie Lu, Shixia Liu:
Scalable Training of Hierarchical Topic Models. Proc. VLDB Endow. 11(7): 826-839 (2018) - [j23]Chao Du, Jun Zhu, Bo Zhang:
Learning Deep Generative Models With Doubly Stochastic Gradient MCMC. IEEE Trans. Neural Networks Learn. Syst. 29(7): 3084-3096 (2018) - [j22]Mengchen Liu, Jiaxin Shi, Kelei Cao, Jun Zhu, Shixia Liu:
Analyzing the Training Processes of Deep Generative Models. IEEE Trans. Vis. Comput. Graph. 24(1): 77-87 (2018) - [j21]Shixia Liu, Jiannan Xiao, Junlin Liu, Xiting Wang, Jing Wu, Jun Zhu:
Visual Diagnosis of Tree Boosting Methods. IEEE Trans. Vis. Comput. Graph. 24(1): 163-173 (2018) - 2017
- [j20]Wenbo Hu, Jun Zhu, Bo Zhang:
Fast sampling methods for Bayesian max-margin models. Expert Syst. Appl. 69: 277-287 (2017) - [j19]Shaohua Li, Jun Zhu, Chunyan Miao:
PSDVec: A toolbox for incremental and scalable word embedding. Neurocomputing 237: 405-409 (2017) - [j18]Tianlin Shi, Jun Zhu:
Online Bayesian Passive-Aggressive Learning. J. Mach. Learn. Res. 18: 33:1-33:39 (2017) - [j17]Jun Zhu, An-An Liu, Mei Chen, Tolga Tasdizen, Hang Su:
Special Issue on Biomedical Big Data: Understanding, Learning and Applications. IEEE Trans. Big Data 3(4): 375-377 (2017) - [j16]Mengchen Liu, Jiaxin Shi, Zhen Li, Chongxuan Li, Jun Zhu, Shixia Liu:
Towards Better Analysis of Deep Convolutional Neural Networks. IEEE Trans. Vis. Comput. Graph. 23(1): 91-100 (2017) - [j15]Shixia Liu, Xiting Wang, Mengchen Liu, Jun Zhu:
Towards better analysis of machine learning models: A visual analytics perspective. Vis. Informatics 1(1): 48-56 (2017) - 2016
- [j14]Wenhao Zhang, Jianqiu Ji, Jun Zhu, Jianmin Li, Hua Xu, Bo Zhang:
BitHash: An efficient bitwise Locality Sensitive Hashing method with applications. Knowl. Based Syst. 97: 40-47 (2016) - [j13]Jianfei Chen, Kaiwei Li, Jun Zhu, Wenguang Chen:
WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation. Proc. VLDB Endow. 9(10): 744-755 (2016) - [j12]Hang Su, Zhaozheng Yin, Seungil Huh, Takeo Kanade, Jun Zhu:
Interactive Cell Segmentation Based on Active and Semi-Supervised Learning. IEEE Trans. Medical Imaging 35(3): 762-777 (2016) - [j11]Xiting Wang, Shixia Liu, Junlin Liu, Jianfei Chen, Jun Zhu, Baining Guo:
TopicPanorama: A Full Picture of Relevant Topics. IEEE Trans. Vis. Comput. Graph. 22(12): 2508-2521 (2016) - 2015
- [j10]Ning Chen, Jun Zhu, Fei Xia, Bo Zhang:
Discriminative Relational Topic Models. IEEE Trans. Pattern Anal. Mach. Intell. 37(5): 973-986 (2015) - 2014
- [j9]Jun Zhu, Ning Chen, Hugh Perkins, Bo Zhang:
Gibbs max-margin topic models with data augmentation. J. Mach. Learn. Res. 15(1): 1073-1110 (2014) - [j8]Jun Zhu, Ning Chen, Eric P. Xing:
Bayesian inference with posterior regularization and applications to infinite latent SVMs. J. Mach. Learn. Res. 15(1): 1799-1847 (2014) - [j7]Ning Chen, Jun Zhu, Fuchun Sun, Bo Zhang:
Learning Harmonium Models With Infinite Latent Features. IEEE Trans. Neural Networks Learn. Syst. 25(3): 520-532 (2014) - 2013
- [j6]Cailiang Liu, Dong Wang, Jun Zhu, Bo Zhang:
Learning a Contextual Multi-Thread Model for Movie/TV Scene Segmentation. IEEE Trans. Multim. 15(4): 884-897 (2013) - 2012
- [j5]Jun Zhu, Amr Ahmed, Eric P. Xing:
MedLDA: maximum margin supervised topic models. J. Mach. Learn. Res. 13: 2237-2278 (2012) - [j4]Min Zhao, Xitao Wang, Hengguo Yu, Jun Zhu:
Dynamics of an ecological model with impulsive control strategy and distributed time delay. Math. Comput. Simul. 82(8): 1432-1444 (2012) - [j3]Ning Chen, Jun Zhu, Fuchun Sun, Eric P. Xing:
Large-Margin Predictive Latent Subspace Learning for Multiview Data Analysis. IEEE Trans. Pattern Anal. Mach. Intell. 34(12): 2365-2378 (2012) - 2009
- [j2]Jun Zhu, Eric P. Xing:
Maximum Entropy Discrimination Markov Networks. J. Mach. Learn. Res. 10: 2531-2569 (2009) - 2008
- [j1]Jun Zhu, Zaiqing Nie, Bo Zhang, Ji-Rong Wen:
Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction. J. Mach. Learn. Res. 9: 1583-1614 (2008)
Conference and Workshop Papers
- 2024
- [c230]Wentse Chen, Shiyu Huang, Yuan Chiang, Tim Pearce, Wei-Wei Tu, Ting Chen, Jun Zhu:
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization. AAAI 2024: 11390-11398 - 2023
- [c229]Shilong Liu, Shijia Huang, Feng Li, Hao Zhang, Yaoyuan Liang, Hang Su, Jun Zhu, Lei Zhang:
DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding. AAAI 2023: 1728-1736 - [c228]Wenze Chen, Shiyu Huang, Yuan Chiang, Ting Chen, Jun Zhu:
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization. AAMAS 2023: 2634-2636 - [c227]Yinpeng Dong, Caixin Kang, Jinlai Zhang, Zijian Zhu, Yikai Wang, Xiao Yang, Hang Su, Xingxing Wei, Jun Zhu:
Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving. CVPR 2023: 1022-1032 - [c226]Xiao Yang, Chang Liu, Longlong Xu, Yikai Wang, Yinpeng Dong, Ning Chen, Hang Su, Jun Zhu:
Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition. CVPR 2023: 4119-4128 - [c225]Jianhui Li, Jianmin Li, Haoji Zhang, Shilong Liu, Zhengyi Wang, Zihao Xiao, Kaiwen Zheng, Jun Zhu:
PREIM3D: 3D Consistent Precise Image Attribute Editing from a Single Image. CVPR 2023: 8549-8558 - [c224]Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, Jun Zhu:
All are Worth Words: A ViT Backbone for Diffusion Models. CVPR 2023: 22669-22679 - [c223]Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M. Ni, Heung-Yeung Shum:
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection. ICLR 2023 - [c222]Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li, Jun Zhu:
Equivariant Energy-Guided SDE for Inverse Molecular Design. ICLR 2023 - [c221]Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su, Jun Zhu:
Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling. ICLR 2023 - [c220]Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Jian Song, Ze Cheng:
Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients. ICLR 2023 - [c219]Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu:
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale. ICML 2023: 1692-1717 - [c218]Zhongkai Hao, Zhengyi Wang, Hang Su, Chengyang Ying, Yinpeng Dong, Songming Liu, Ze Cheng, Jian Song, Jun Zhu:
GNOT: A General Neural Operator Transformer for Operator Learning. ICML 2023: 12556-12569 - [c217]Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Ze Cheng, Jun Zhu:
NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data. ICML 2023: 21658-21671 - [c216]Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, Jun Zhu:
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning. ICML 2023: 22825-22855 - [c215]Jiachen Yao, Chang Su, Zhongkai Hao, Songming Liu, Hang Su, Jun Zhu:
MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks. ICML 2023: 39702-39721 - [c214]Kaiwen Zheng, Cheng Lu, Jianfei Chen, Jun Zhu:
Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs. ICML 2023: 42363-42389 - [c213]Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Dong Yan, Jun Zhu:
On the Reuse Bias in Off-Policy Reinforcement Learning. IJCAI 2023: 4513-4521 - [c212]Yingtao Luo, Qiang Liu, Yuntian Chen, Wenbo Hu, Tian Tian, Jun Zhu:
Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations. KDD 2023: 1595-1607 - [c211]Zhijie Deng, Peng Cui, Jun Zhu:
Towards Accelerated Model Training via Bayesian Data Selection. NeurIPS 2023 - [c210]Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, Jun Zhu:
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation. NeurIPS 2023 - [c209]Ziyu Wang, Binjie Yuan, Jiaxun Lu, Bowen Ding, Yunfeng Shao, Qibin Wu, Jun Zhu:
A constrained Bayesian approach to out-of-distribution prediction. UAI 2023: 2248-2258 - 2022
- [c208]Jialian Li, Tongzheng Ren, Dong Yan, Hang Su, Jun Zhu:
Policy Learning for Robust Markov Decision Process with a Mismatched Generative Model. AAAI 2022: 7417-7425 - [c207]Shiyu Huang, Chao Yu, Bin Wang, Dong Li, Yu Wang, Ting Chen, Jun Zhu:
VMAPD: Generate Diverse Solutions for Multi-Agent Games with Recurrent Trajectory Discriminators. CoG 2022: 9-16 - [c206]Hongyang Gu, Jianmin Li, Guangyuan Fu, Chifong Wong, Xinghao Chen, Jun Zhu:
AutoLoss-GMS: Searching Generalized Margin-based Softmax Loss Function for Person Re-identification. CVPR 2022: 4734-4743 - [c205]Nanyang Ye, Kaican Li, Haoyue Bai, Runpeng Yu, Lanqing Hong, Fengwei Zhou, Zhenguo Li, Jun Zhu:
OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization. CVPR 2022: 7937-7948 - [c204]Tianyu Pang, Huishuai Zhang, Di He, Yinpeng Dong, Hang Su, Wei Chen, Jun Zhu, Tie-Yan Liu:
Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart. CVPR 2022: 15202-15212 - [c203]Liyuan Wang, Xingxing Zhang, Qian Li, Jun Zhu, Yi Zhong:
CoSCL: Cooperation of Small Continual Learners is Stronger Than a Big One. ECCV (26) 2022: 254-271 - [c202]Shih-Han Chan, Yinpeng Dong, Jun Zhu, Xiaolu Zhang, Jun Zhou:
BadDet: Backdoor Attacks on Object Detection. ECCV Workshops (1) 2022: 396-412 - [c201]Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu:
Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks. ECCV (4) 2022: 725-742 - [c200]Fan Bao, Chongxuan Li, Jun Zhu, Bo Zhang:
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models. ICLR 2022 - [c199]Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, Jun Zhu:
Exploring Memorization in Adversarial Training. ICLR 2022 - [c198]Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, Lei Zhang:
DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR. ICLR 2022 - [c197]Liyuan Wang, Xingxing Zhang, Kuo Yang, Longhui Yu, Chongxuan Li, Lanqing Hong, Shifeng Zhang, Zhenguo Li, Yi Zhong, Jun Zhu:
Memory Replay with Data Compression for Continual Learning. ICLR 2022 - [c196]Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu, Bo Zhang:
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models. ICML 2022: 1555-1584 - [c195]Zhijie Deng, Jiaxin Shi, Jun Zhu:
NeuralEF: Deconstructing Kernels by Deep Neural Networks. ICML 2022: 4976-4992 - [c194]Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jian Song, Jun Zhu:
GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing. ICML 2022: 8465-8483 - [c193]Cheng Lu, Kaiwen Zheng, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu:
Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching. ICML 2022: 14429-14460 - [c192]Tianyu Pang, Min Lin, Xiao Yang, Jun Zhu, Shuicheng Yan:
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition. ICML 2022: 17258-17277 - [c191]Siyu Wang, Jianfei Chen, Chongxuan Li, Jun Zhu, Bo Zhang:
Fast Lossless Neural Compression with Integer-Only Discrete Flows. ICML 2022: 22562-22575 - [c190]Zhengyi Wang, Zhongkai Hao, Ziqiao Wang, Hang Su, Jun Zhu:
Cluster Attack: Query-based Adversarial Attacks on Graph with Graph-Dependent Priors. IJCAI 2022: 768-775 - [c189]Chengyang Ying, Xinning Zhou, Hang Su, Dong Yan, Ning Chen, Jun Zhu:
Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk. IJCAI 2022: 3673-3680 - [c188]Xingxing Zhang, Zhizhe Liu, Weikai Yang, Liyuan Wang, Jun Zhu:
The More, The Better? Active Silencing of Non-Positive Transfer for Efficient Multi-Domain Few-Shot Classification. ACM Multimedia 2022: 1993-2001 - [c187]Min Zhao, Fan Bao, Chongxuan Li, Jun Zhu:
EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations. NeurIPS 2022 - [c186]Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu:
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps. NeurIPS 2022 - [c185]Peng Cui, Yang Yue, Zhijie Deng, Jun Zhu:
Confidence-based Reliable Learning under Dual Noises. NeurIPS 2022 - [c184]Zhijie Deng, Feng Zhou, Jun Zhu:
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning. NeurIPS 2022 - [c183]Yinpeng Dong, Shouwei Ruan, Hang Su, Caixin Kang, Xingxing Wei, Jun Zhu:
ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints. NeurIPS 2022 - [c182]Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Ze Cheng:
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs. NeurIPS 2022 - [c181]Yibo Miao, Yinpeng Dong, Jun Zhu, Xiao-Shan Gao:
Isometric 3D Adversarial Examples in the Physical World. NeurIPS 2022 - [c180]Ziyu Wang, Yuhao Zhou, Jun Zhu:
Fast Instrument Learning with Faster Rates. NeurIPS 2022 - [c179]Qi-An Fu, Yinpeng Dong, Hang Su, Jun Zhu, Chao Zhang:
AutoDA: Automated Decision-based Iterative Adversarial Attacks. USENIX Security Symposium 2022: 3557-3574 - 2021
- [c178]Yong Ren, Yucen Luo, Jun Zhu:
Improving Generative Moment Matching Networks with Distribution Partition. AAAI 2021: 9403-9410 - [c177]Haosheng Zou, Tongzheng Ren, Dong Yan, Hang Su, Jun Zhu:
Learning Task-Distribution Reward Shaping with Meta-Learning. AAAI 2021: 11210-11218 - [c176]Qipeng Guo, Zhijing Jin, Ziyu Wang, Xipeng Qiu, Weinan Zhang, Jun Zhu, Zheng Zhang, David Wipf:
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings. AISTATS 2021: 1828-1836 - [c175]Qiang Liu, Zhaocheng Liu, Haoli Zhang, Yuntian Chen, Jun Zhu:
Mining Cross Features for Financial Credit Risk Assessment. CIKM 2021: 1069-1078 - [c174]Zhijie Deng, Xiao Yang, Shizhen Xu, Hang Su, Jun Zhu:
LiBRe: A Practical Bayesian Approach to Adversarial Detection. CVPR 2021: 972-982 - [c173]Liyuan Wang, Kuo Yang, Chongxuan Li, Lanqing Hong, Zhenguo Li, Jun Zhu:
ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-Supervised Continual Learning. CVPR 2021: 5383-5392 - [c172]Shilong Liu, Lei Zhang, Xiao Yang, Hang Su, Jun Zhu:
Unsupervised Part Segmentation Through Disentangling Appearance and Shape. CVPR 2021: 8355-8364 - [c171]Zihao Xiao, Xianfeng Gao, Chilin Fu, Yinpeng Dong, Wei Gao, Xiaolu Zhang, Jun Zhou, Jun Zhu:
Improving Transferability of Adversarial Patches on Face Recognition With Generative Models. CVPR 2021: 11845-11854 - [c170]Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu, Yuefeng Chen, Hui Xue:
Towards Face Encryption by Generating Adversarial Identity Masks. ICCV 2021: 3877-3887 - [c169]Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, Jun Zhu:
Black-box Detection of Backdoor Attacks with Limited Information and Data. ICCV 2021: 16462-16471 - [c168]Cheng Lu, Jianfei Chen, Chongxuan Li, Qiuhao Wang, Jun Zhu:
Implicit Normalizing Flows. ICLR 2021 - [c167]Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu:
Bag of Tricks for Adversarial Training. ICLR 2021 - [c166]Tsung Wei Tsai, Chongxuan Li, Jun Zhu:
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering. ICLR 2021 - [c165]Fan Bao, Kun Xu, Chongxuan Li, Lanqing Hong, Jun Zhu, Bo Zhang:
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models. ICML 2021: 651-661 - [c164]Yunsheng Zhang, Dong Yan, Bei Shi, Haobo Fu, Qiang Fu, Hang Su, Jun Zhu, Ning Chen:
Combining Tree Search and Action Prediction for State-of-the-Art Performance in DouDiZhu. IJCAI 2021: 3413-3419 - [c163]Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu:
Accumulative Poisoning Attacks on Real-time Data. NeurIPS 2021: 2899-2912 - [c162]Fan Bao, Guoqiang Wu, Chongxuan Li, Jun Zhu, Bo Zhang:
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization. NeurIPS 2021: 4529-4541 - [c161]Ziyu Wang, Yuhao Zhou, Tongzheng Ren, Jun Zhu:
Scalable Quasi-Bayesian Inference for Instrumental Variable Regression. NeurIPS 2021: 10469-10482 - [c160]Guoqiang Wu, Chongxuan Li, Kun Xu, Jun Zhu:
Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization. NeurIPS 2021: 14332-14344 - [c159]Shuyu Cheng, Guoqiang Wu, Jun Zhu:
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms. NeurIPS 2021: 14620-14631 - [c158]Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, Yi Zhong:
AFEC: Active Forgetting of Negative Transfer in Continual Learning. NeurIPS 2021: 22379-22391 - [c157]Shiyu Huang, Bin Wang, Hang Su, Dong Li, Jianye Hao, Jun Zhu, Ting Chen:
Off-Policy Training for Truncated TD(λ) Boosted Soft Actor-Critic. PRICAI (3) 2021: 46-59 - 2020
- [c156]Ziyu Wang, Shuyu Cheng, Yueru Li, Jun Zhu, Bo Zhang:
A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models. AISTATS 2020: 3728-3738 - [c155]Yinpeng Dong, Qi-An Fu, Xiao Yang, Tianyu Pang, Hang Su, Zihao Xiao, Jun Zhu:
Benchmarking Adversarial Robustness on Image Classification. CVPR 2020: 318-328 - [c154]Xiao Yang, Fangyun Wei, Hongyang Zhang, Jun Zhu:
Design and Interpretation of Universal Adversarial Patches in Face Detection. ECCV (17) 2020: 174-191 - [c153]Haoyu Liang, Zhihao Ouyang, Yuyuan Zeng, Hang Su, Zihao He, Shu-Tao Xia, Jun Zhu, Bo Zhang:
Training Interpretable Convolutional Neural Networks by Differentiating Class-Specific Filters. ECCV (2) 2020: 622-638 - [c152]Yueru Li, Shuyu Cheng, Hang Su, Jun Zhu:
Defense Against Adversarial Attacks via Controlling Gradient Leaking on Embedded Manifolds. ECCV (28) 2020: 753-769 - [c151]Ziyu Wang, Bin Dai, David Wipf, Jun Zhu:
Further Analysis of Outlier Detection with Deep Generative Models. ICBINB@NeurIPS 2020: 11-20 - [c150]Shiyu Huang, Hang Su, Jun Zhu, Ting Chen:
SVQN: Sequential Variational Soft Q-Learning Networks. ICLR 2020 - [c149]Chongxuan Li, Chao Du, Kun Xu, Max Welling, Jun Zhu, Bo Zhang:
To Relieve Your Headache of Training an MRF, Take AdVIL. ICLR 2020 - [c148]Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen:
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. ICLR 2020 - [c147]Tianyu Pang, Kun Xu, Yinpeng Dong, Chao Du, Ning Chen, Jun Zhu:
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness. ICLR 2020 - [c146]Tianyu Pang, Kun Xu, Jun Zhu:
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks. ICLR 2020 - [c145]Yichi Zhou, Jialian Li, Jun Zhu:
Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information. ICLR 2020 - [c144]Yichi Zhou, Tongzheng Ren, Jialian Li, Dong Yan, Jun Zhu:
Lazy-CFR: fast and near-optimal regret minimization for extensive games with imperfect information. ICLR 2020 - [c143]Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, Tian Tian:
VFlow: More Expressive Generative Flows with Variational Data Augmentation. ICML 2020: 1660-1669 - [c142]Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang:
Understanding and Stabilizing GANs' Training Dynamics Using Control Theory. ICML 2020: 10566-10575 - [c141]Yuhao Zhou, Jiaxin Shi, Jun Zhu:
Nonparametric Score Estimators. ICML 2020: 11513-11522 - [c140]Michael Zhu, Chang Liu, Jun Zhu:
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference. ICML 2020: 11576-11587 - [c139]Yanze Min, Hang Su, Jun Zhu, Bo Zhang:
Discrete Memory Addressing Variational Autoencoder for Visual Concept Learning. IJCNN 2020: 1-8 - [c138]Ziyu Wang, Bin Dai, David P. Wipf, Jun Zhu:
Further Analysis of Outlier Detection with Deep Generative Models. NeurIPS 2020 - [c137]Fan Bao, Chongxuan Li, Taufik Xu, Hang Su, Jun Zhu, Bo Zhang:
Bi-level Score Matching for Learning Energy-based Latent Variable Models. NeurIPS 2020 - [c136]Peng Cui, Wenbo Hu, Jun Zhu:
Calibrated Reliable Regression using Maximum Mean Discrepancy. NeurIPS 2020 - [c135]Zhijie Deng, Yinpeng Dong, Shifeng Zhang, Jun Zhu:
Understanding and Exploring the Network with Stochastic Architectures. NeurIPS 2020 - [c134]Yinpeng Dong, Zhijie Deng, Tianyu Pang, Jun Zhu, Hang Su:
Adversarial Distributional Training for Robust Deep Learning. NeurIPS 2020 - [c133]Tianyu Pang, Taufik Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu:
Efficient Learning of Generative Models via Finite-Difference Score Matching. NeurIPS 2020 - [c132]Tianyu Pang, Xiao Yang, Yinpeng Dong, Taufik Xu, Jun Zhu, Hang Su:
Boosting Adversarial Training with Hypersphere Embedding. NeurIPS 2020 - [c131]Guoqiang Wu, Jun Zhu:
Multi-label classification: do Hamming loss and subset accuracy really conflict with each other? NeurIPS 2020 - [c130]Kun Xu, Chao Du, Chongxuan Li, Jun Zhu, Bo Zhang:
Learning Implicit Generative Models by Teaching Density Estimators. ECML/PKDD (2) 2020: 239-255 - [c129]Jialian Li, Yichi Zhou, Tongzheng Ren, Jun Zhu:
Exploration Analysis in Finite-Horizon Turn-based Stochastic Games. UAI 2020: 201-210 - 2019
- [c128]Shiyu Huang, Hang Su, Jun Zhu, Ting Chen:
Combo-Action: Training Agent For FPS Game with Auxiliary Tasks. AAAI 2019: 954-961 - [c127]Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Yuan Xie, Luping Shi:
Direct Training for Spiking Neural Networks: Faster, Larger, Better. AAAI 2019: 1311-1318 - [c126]You Qiaoben, Zheng Wang, Jianguo Li, Yinpeng Dong, Yu-Gang Jiang, Jun Zhu:
Composite Binary Decomposition Networks. AAAI 2019: 4747-4754 - [c125]Xingxing Wei, Jun Zhu, Sha Yuan, Hang Su:
Sparse Adversarial Perturbations for Videos. AAAI 2019: 8973-8980 - [c124]Jialian Li, Tongzheng Ren, Hang Su, Jun Zhu:
Learn a Robust Policy in Adversarial Games via Playing with an Expert Opponent. AAMAS 2019: 2096-2098 - [c123]Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu:
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks. CVPR 2019: 4312-4321 - [c122]Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, Jun Zhu:
Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition. CVPR 2019: 7714-7722 - [c121]Zhijie Deng, Yucen Luo, Jun Zhu:
Cluster Alignment With a Teacher for Unsupervised Domain Adaptation. ICCV 2019: 9943-9952 - [c120]Ziyu Wang, Tongzheng Ren, Jun Zhu, Bo Zhang:
Function Space Particle Optimization for Bayesian Neural Networks. ICLR (Poster) 2019 - [c119]Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu:
Understanding and Accelerating Particle-Based Variational Inference. ICML 2019: 4082-4092 - [c118]Chang Liu, Jingwei Zhuo, Jun Zhu:
Understanding MCMC Dynamics as Flows on the Wasserstein Space. ICML 2019: 4093-4103 - [c117]Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu:
Improving Adversarial Robustness via Promoting Ensemble Diversity. ICML 2019: 4970-4979 - [c116]Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu:
Scalable Training of Inference Networks for Gaussian-Process Models. ICML 2019: 5758-5768 - [c115]Shihong Song, Jiayi Weng, Hang Su, Dong Yan, Haosheng Zou, Jun Zhu:
Playing FPS Games With Environment-Aware Hierarchical Reinforcement Learning. IJCAI 2019: 3475-3482 - [c114]Taufik Xu, Chongxuan Li, Jun Zhu, Bo Zhang:
Multi-objects Generation with Amortized Structural Regularization. NeurIPS 2019: 6615-6625 - [c113]Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu:
Improving Black-box Adversarial Attacks with a Transfer-based Prior. NeurIPS 2019: 10932-10942 - [c112]Feiyu Xu, Hans Uszkoreit, Yangzhou Du, Wei Fan, Dongyan Zhao, Jun Zhu:
Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges. NLPCC (2) 2019: 563-574 - 2018
- [c111]Chao Du, Chongxuan Li, Yin Zheng, Jun Zhu, Bo Zhang:
Collaborative Filtering With User-Item Co-Autoregressive Models. AAAI 2018: 2175-2182 - [c110]Chang Liu, Jun Zhu:
Riemannian Stein Variational Gradient Descent for Bayesian Inference. AAAI 2018: 3627-3634 - [c109]Tian Tian, Yichi Zhou, Jun Zhu:
Selective Verification Strategy for Learning From Crowds. AAAI 2018: 4147-4154 - [c108]Zihao Xiao, Jianfei Chen, Jun Zhu:
Towards Training Probabilistic Topic Models on Neuromorphic Multi-Chip Systems. AAAI 2018: 6459-6467 - [c107]Haosheng Zou, Hang Su, Shihong Song, Jun Zhu:
Understanding Human Behaviors in Crowds by Imitating the Decision-Making Process. AAAI 2018: 7648-7656 - [c106]Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, Jun Zhu:
Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser. CVPR 2018: 1778-1787 - [c105]Juzheng Li, Hang Su, Jun Zhu, Siyu Wang, Bo Zhang:
Textbook Question Answering Under Instructor Guidance With Memory Networks. CVPR 2018: 3655-3663 - [c104]Yucen Luo, Jun Zhu, Mengxi Li, Yong Ren, Bo Zhang:
Smooth Neighbors on Teacher Graphs for Semi-Supervised Learning. CVPR 2018: 8896-8905 - [c103]Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, Jianguo Li:
Boosting Adversarial Attacks With Momentum. CVPR 2018: 9185-9193 - [c102]Jiaxin Shi, Shengyang Sun, Jun Zhu:
Kernel Implicit Variational Inference. ICLR (Poster) 2018 - [c101]Juzheng Li, Hang Su, Jun Zhu, Bo Zhang:
Essay-Anchor Attentive Multi-Modal Bilinear Pooling for Textbook Question Answering. ICME 2018: 1-6 - [c100]Jianfei Chen, Jun Zhu, Le Song:
Stochastic Training of Graph Convolutional Networks with Variance Reduction. ICML 2018: 941-949 - [c99]Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song:
Adversarial Attack on Graph Structured Data. ICML 2018: 1123-1132 - [c98]Tianyu Pang, Chao Du, Jun Zhu:
Max-Mahalanobis Linear Discriminant Analysis Networks. ICML 2018: 4013-4022 - [c97]Jiaxin Shi, Shengyang Sun, Jun Zhu:
A Spectral Approach to Gradient Estimation for Implicit Distributions. ICML 2018: 4651-4660 - [c96]Yichi Zhou, Jun Zhu, Jingwei Zhuo:
Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors. ICML 2018: 5995-6003 - [c95]Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, Bo Zhang:
Message Passing Stein Variational Gradient Descent. ICML 2018: 6013-6022 - [c94]Mengchen Liu, Shixia Liu, Hang Su, Kelei Cao, Jun Zhu:
Analyzing the Noise Robustness of Deep Neural Networks. VAST 2018: 60-71 - [c93]Danyang Sun, Tongzheng Ren, Chongxuan Li, Hang Su, Jun Zhu:
Learning to Write Stylized Chinese Characters by Reading a Handful of Examples. IJCAI 2018: 920-927 - [c92]Jun Zhu:
Probabilistic Machine Learning: Models, Algorithms and a Programming Library. IJCAI 2018: 5754-5759 - [c91]Xingxing Wei, Jun Zhu, Sitong Feng, Hang Su:
Video-to-Video Translation with Global Temporal Consistency. ACM Multimedia 2018: 18-25 - [c90]Yucen Luo, Tian Tian, Jiaxin Shi, Jun Zhu, Bo Zhang:
Semi-crowdsourced Clustering with Deep Generative Models. NeurIPS 2018: 3216-3226 - [c89]Tianyu Pang, Chao Du, Yinpeng Dong, Jun Zhu:
Towards Robust Detection of Adversarial Examples. NeurIPS 2018: 4584-4594 - [c88]Chongxuan Li, Max Welling, Jun Zhu, Bo Zhang:
Graphical Generative Adversarial Networks. NeurIPS 2018: 6072-6083 - [c87]Jianfei Chen, Jun Zhu, Yee Whye Teh, Tong Zhang:
Stochastic Expectation Maximization with Variance Reduction. NeurIPS 2018: 7978-7988 - [c86]Mowei Wang, Yong Cui, Shihan Xiao, Xin Wang, Dan Yang, Kai Chen, Jun Zhu:
Neural Network Meets DCN: Traffic-driven Topology Adaptation with Deep Learning. SIGMETRICS (Abstracts) 2018: 97-99 - 2017
- [c85]Tian Tian, Ning Chen, Jun Zhu:
Learning Attributes from the Crowdsourced Relative Labels. AAAI 2017: 1562-1568 - [c84]Kaiwei Li, Jianfei Chen, Wenguang Chen, Jun Zhu:
SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs. ASPLOS 2017: 497-509 - [c83]Yinpeng Dong, Hang Su, Jun Zhu, Bo Zhang:
Improving Interpretability of Deep Neural Networks with Semantic Information. CVPR 2017: 975-983 - [c82]Yichi Zhou, Jialian Li, Jun Zhu:
Identify the Nash Equilibrium in Static Games with Random Payoffs. ICML 2017: 4160-4169 - [c81]Wenbo Hu, Jun Zhu, Hang Su, Jingwei Zhuo, Bo Zhang:
Semi-supervised Max-margin Topic Model with Manifold Posterior Regularization. IJCAI 2017: 1865-1871 - [c80]Mengchen Liu, Liu Jiang, Junlin Liu, Xiting Wang, Jun Zhu, Shixia Liu:
Improving Learning-from-Crowds through Expert Validation. IJCAI 2017: 2329-2336 - [c79]Yong Ren, Jun Zhu:
Distributed Accelerated Proximal Coordinate Gradient Methods. IJCAI 2017: 2655-2661 - [c78]Hang Su, Jun Zhu, Yinpeng Dong, Bo Zhang:
Forecast the Plausible Paths in Crowd Scenes. IJCAI 2017: 2772-2778 - [c77]Zhijie Deng, Hao Zhang, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu, Eric P. Xing:
Structured Generative Adversarial Networks. NIPS 2017: 3899-3909 - [c76]Chongxuan Li, Taufik Xu, Jun Zhu, Bo Zhang:
Triple Generative Adversarial Nets. NIPS 2017: 4088-4098 - [c75]Jianfei Chen, Chongxuan Li, Yizhong Ru, Jun Zhu:
Population Matching Discrepancy and Applications in Deep Learning. NIPS 2017: 6262-6272 - 2016
- [c74]Bei Chen, Ning Chen, Jun Zhu, Jiaming Song, Bo Zhang:
Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation. AAAI 2016: 1153-1159 - [c73]Yang Song, Jun Zhu:
Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization. AAAI 2016: 2044-2050 - [c72]Bei Chen, Jun Zhu, Nan Yang, Tian Tian, Ming Zhou, Bo Zhang:
Jointly Modeling Topics and Intents with Global Order Structure. AAAI 2016: 2711-2717 - [c71]Shaohua Li, Tat-Seng Chua, Jun Zhu, Chunyan Miao:
Generative Topic Embedding: a Continuous Representation of Documents. ACL (1) 2016 - [c70]Juzheng Li, Jun Zhu, Bo Zhang:
Discriminative Deep Random Walk for Network Classification. ACL (1) 2016 - [c69]Jingwei Zhuo, Yong Cao, Jun Zhu, Bo Zhang, Zaiqing Nie:
Segment-Level Sequence Modeling using Gated Recursive Semi-Markov Conditional Random Fields. ACL (1) 2016 - [c68]Kun Xu, Hang Su, Jun Zhu, Ji-Song Guan, Bo Zhang:
Neuron Segmentation Based on CNN with Semi-Supervised Regularization. CVPR Workshops 2016: 1324-1332 - [c67]Hang Su, Jun Zhu, Zhaozheng Yin, Yinpeng Dong, Bo Zhang:
Efficient and Robust Semi-supervised Learning Over a Sparse-Regularized Graph. ECCV (8) 2016: 583-598 - [c66]Pengtao Xie, Jun Zhu, Eric P. Xing:
Diversity-Promoting Bayesian Learning of Latent Variable Models. ICML 2016: 59-68 - [c65]Chongxuan Li, Jun Zhu, Bo Zhang:
Learning to Generate with Memory. ICML 2016: 1177-1186 - [c64]Hang Su, Yinpeng Dong, Jun Zhu, Haibin Ling, Bo Zhang:
Crowd Scene Understanding with Coherent Recurrent Neural Networks. IJCAI 2016: 3469-3476 - [c63]Yuan Yang, Jianfei Chen, Jun Zhu:
Distributing the Stochastic Gradient Sampler for Large-Scale LDA. KDD 2016: 1975-1984 - [c62]Yong Ren, Jun Zhu, Jialian Li, Yucen Luo:
Conditional Generative Moment-Matching Networks. NIPS 2016: 2928-2936 - [c61]Chang Liu, Jun Zhu, Yang Song:
Stochastic Gradient Geodesic MCMC Methods. NIPS 2016: 3009-3017 - [c60]Yang Song, Jun Zhu, Yong Ren:
Kernel Bayesian Inference with Posterior Regularization. NIPS 2016: 4763-4771 - [c59]Arnab Bhadury, Jianfei Chen, Jun Zhu, Shixia Liu:
Scaling up Dynamic Topic Models. WWW 2016: 381-390 - 2015
- [c58]Wenhao Zhang, Jianqiu Ji, Jun Zhu, Hua Xu, Bo Zhang:
Large Scale Sentiment Analysis with Locality Sensitive BitHash. AIRS 2015: 29-40 - [c57]Shaohua Li, Jun Zhu, Chunyan Miao:
A Generative Word Embedding Model and its Low Rank Positive Semidefinite Solution. EMNLP 2015: 1599-1609 - [c56]Yining Wang, Jun Zhu:
DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics. ICML 2015: 862-870 - [c55]Jingwei Zhuo, Jun Zhu, Bo Zhang:
Adaptive Dropout Rates for Learning with Corrupted Features. IJCAI 2015: 4126-4133 - [c54]Qi Lyu, Zhiyong Wu, Jun Zhu, Helen Meng:
Modelling High-Dimensional Sequences with LSTM-RTRBM: Application to Polyphonic Music Generation. IJCAI 2015: 4138-4139 - [c53]Qi Lyu, Zhiyong Wu, Jun Zhu:
Polyphonic Music Modelling with LSTM-RTRBM. ACM Multimedia 2015: 991-994 - [c52]Chongxuan Li, Jun Zhu, Tianlin Shi, Bo Zhang:
Max-Margin Deep Generative Models. NIPS 2015: 1837-1845 - [c51]Jiaxin Shi, Jun Zhu:
Building Memory with Concept Learning Capabilities from Large-Scale Knowledge Bases. CoCo@NIPS 2015 - [c50]Tian Tian, Jun Zhu:
Max-Margin Majority Voting for Learning from Crowds. NIPS 2015: 1621-1629 - [c49]Tian Tian, Jun Zhu:
Uncovering the Latent Structures of Crowd Labeling. PAKDD (1) 2015: 392-404 - [c48]Tian Tian, Jun Zhu, Fen Xia, Xin Zhuang, Tong Zhang:
Crowd Fraud Detection in Internet Advertising. WWW 2015: 1100-1110 - 2014
- [c47]Ning Chen, Jun Zhu, Jianfei Chen, Bo Zhang:
Dropout Training for Support Vector Machines. AAAI 2014: 1752-1759 - [c46]Yining Wang, Jun Zhu:
Small-Variance Asymptotics for Dirichlet Process Mixtures of SVMs. AAAI 2014: 2135-2141 - [c45]Shike Mei, Jun Zhu, Jerry Zhu:
Robust RegBayes: Selectively Incorporating First-Order Logic Domain Knowledge into Bayesian Models. ICML 2014: 253-261 - [c44]Aonan Zhang, Jun Zhu, Bo Zhang:
Max-Margin Infinite Hidden Markov Models. ICML 2014: 315-323 - [c43]Tianlin Shi, Jun Zhu:
Online Bayesian Passive-Aggressive Learning. ICML 2014: 378-386 - [c42]Chengtao Li, Jun Zhu, Jianfei Chen:
Bayesian Max-margin Multi-Task Learning with Data Augmentation. ICML 2014: 415-423 - [c41]Fei Xia, Ning Chen, Jun Zhu, Aonan Zhang, Xiaoming Jin:
Max-margin latent feature relational models for entity-attribute networks. IJCNN 2014: 1667-1674 - [c40]Changyou Chen, Jun Zhu, Xinhua Zhang:
Robust Bayesian Max-Margin Clustering. NIPS 2014: 532-540 - [c39]Yining Wang, Jun Zhu:
Spectral Methods for Supervised Topic Models. NIPS 2014: 1511-1519 - [c38]Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang:
Distributed Bayesian Posterior Sampling via Moment Sharing. NIPS 2014: 3356-3364 - [c37]Renjie Liao, Jun Zhu, Zengchang Qin:
Nonparametric bayesian upstream supervised multi-modal topic models. WSDM 2014: 493-502 - 2013
- [c36]Jun Zhu, Xun Zheng, Bo Zhang:
Improved Bayesian Logistic Supervised Topic Models with Data Augmentation. ACL (1) 2013: 187-195 - [c35]Minjie Xu, Jun Zhu:
Discriminative infinite latent feature models. ChinaSIP 2013: 184-188 - [c34]Jun Zhu, Ning Chen, Hugh Perkins, Bo Zhang:
Gibbs Max-Margin Topic Models with Fast Sampling Algorithms. ICML (1) 2013: 124-132 - [c33]Minjie Xu, Jun Zhu, Bo Zhang:
Fast Max-Margin Matrix Factorization with Data Augmentation. ICML (3) 2013: 978-986 - [c32]Ning Chen, Jun Zhu, Fei Xia, Bo Zhang:
Generalized Relational Topic Models with Data Augmentation. IJCAI 2013: 1273-1279 - [c31]Jun Zhu, Xun Zheng, Li Zhou, Bo Zhang:
Scalable inference in max-margin topic models. KDD 2013: 964-972 - [c30]Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng, Bo Zhang:
Scalable Inference for Logistic-Normal Topic Models. NIPS 2013: 2445-2453 - [c29]Aonan Zhang, Jun Zhu, Bo Zhang:
Sparse Relational Topic Models for Document Networks. ECML/PKDD (1) 2013: 670-685 - [c28]Aonan Zhang, Jun Zhu, Bo Zhang:
Sparse online topic models. WWW 2013: 1489-1500 - 2012
- [c27]Li-Jia Li, Jun Zhu, Hao Su, Eric P. Xing, Li Fei-Fei:
Multi-Level Structured Image Coding on High-Dimensional Image Representation. ACCV (2) 2012: 147-161 - [c26]Jun Zhu:
Max-Margin Nonparametric Latent Feature Models for Link Prediction. ICML 2012 - [c25]Yuandong Tian, Jun Zhu:
Learning from crowds in the presence of schools of thought. KDD 2012: 226-234 - [c24]Minjie Xu, Jun Zhu, Bo Zhang:
Nonparametric Max-Margin Matrix Factorization for Collaborative Prediction. NIPS 2012: 64-72 - [c23]Qixia Jiang, Jun Zhu, Maosong Sun, Eric P. Xing:
Monte Carlo Methods for Maximum Margin Supervised Topic Models. NIPS 2012: 1601-1609 - 2011
- [c22]Jun Zhu, Ning Chen, Eric P. Xing:
Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel Machines. ICML 2011: 617-624 - [c21]Jun Zhu, Ni Lao, Ning Chen, Eric P. Xing:
Conditional topical coding: an efficient topic model conditioned on rich features. KDD 2011: 475-483 - [c20]Jun Zhu, Ning Chen, Eric P. Xing:
Infinite Latent SVM for Classification and Multi-task Learning. NIPS 2011: 1620-1628 - [c19]Jun Zhu, Eric P. Xing:
Sparse Topical Coding. UAI 2011: 831-838 - 2010
- [c18]Jun Zhu, Eric P. Xing:
Conditional Topic Random Fields. ICML 2010: 1239-1246 - [c17]Jun Zhu, Ni Lao, Eric P. Xing:
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields. KDD 2010: 303-312 - [c16]Ning Chen, Jun Zhu, Eric P. Xing:
Predictive Subspace Learning for Multi-view Data: a Large Margin Approach. NIPS 2010: 361-369 - [c15]Ni Lao, Jun Zhu, Xinwang Liu, Yandong Liu, William W. Cohen:
Efficient Relational Learning with Hidden Variable Detection. NIPS 2010: 1234-1242 - [c14]Seunghak Lee, Jun Zhu, Eric P. Xing:
Adaptive Multi-Task Lasso: with Application to eQTL Detection. NIPS 2010: 1306-1314 - [c13]Jun Zhu, Li-Jia Li, Li Fei-Fei, Eric P. Xing:
Large Margin Learning of Upstream Scene Understanding Models. NIPS 2010: 2586-2594 - 2009
- [c12]Jun Zhu, Amr Ahmed, Eric P. Xing:
MedLDA: maximum margin supervised topic models for regression and classification. ICML 2009: 1257-1264 - [c11]Jun Zhu, Eric P. Xing:
On primal and dual sparsity of Markov networks. ICML 2009: 1265-1272 - [c10]Xiaolin Shi, Jun Zhu, Rui Cai, Lei Zhang:
User grouping behavior in online forums. KDD 2009: 777-786 - [c9]Jun Zhu, Eric P. Xing, Bo Zhang:
Primal sparse Max-margin Markov networks. KDD 2009: 1047-1056 - [c8]Jun Zhu, Zaiqing Nie, Xiaojiang Liu, Bo Zhang, Ji-Rong Wen:
StatSnowball: a statistical approach to extracting entity relationships. WWW 2009: 101-110 - [c7]Jiang-Ming Yang, Rui Cai, Yida Wang, Jun Zhu, Lei Zhang, Wei-Ying Ma:
Incorporating site-level knowledge to extract structured data from web forums. WWW 2009: 181-190 - 2008
- [c6]Jun Zhu, Eric P. Xing, Bo Zhang:
Laplace maximum margin Markov networks. ICML 2008: 1256-1263 - [c5]Jun Zhu, Eric P. Xing, Bo Zhang:
Partially Observed Maximum Entropy Discrimination Markov Networks. NIPS 2008: 1977-1984 - 2007
- [c4]Jun Zhu, Zaiqing Nie, Bo Zhang, Ji-Rong Wen:
Dynamic hierarchical Markov random fields and their application to web data extraction. ICML 2007: 1175-1182 - [c3]Jun Zhu, Bo Zhang, Zaiqing Nie, Ji-Rong Wen, Hsiao-Wuen Hon:
Webpage understanding: an integrated approach. KDD 2007: 903-912 - 2006
- [c2]Jun Zhu, Zaiqing Nie, Ji-Rong Wen, Bo Zhang, Wei-Ying Ma:
Simultaneous record detection and attribute labeling in web data extraction. KDD 2006: 494-503 - 2005
- [c1]Jun Zhu, Zaiqing Nie, Ji-Rong Wen, Bo Zhang, Wei-Ying Ma:
2D Conditional Random Fields for Web information extraction. ICML 2005: 1044-1051
Editorship
- 2018
- [e1]Jun Zhu, Ichiro Takeuchi:
Proceedings of The 10th Asian Conference on Machine Learning, ACML 2018, Beijing, China, November 14-16, 2018. Proceedings of Machine Learning Research 95, PMLR 2018 [contents]
Reference Works
- 2014
- [r2]Jun Zhu, Eric P. Xing:
Discriminative Training of Mixed Membership Models. Handbook of Mixed Membership Models and Their Applications 2014: 369-393 - 2009
- [r1]Jun Zhu, Zaiqing Nie, Bo Zhang:
Statistical Web Object Extraction. Encyclopedia of Data Warehousing and Mining 2009: 1854-1858
Informal and Other Publications
- 2023
- [i228]Liyuan Wang, Xingxing Zhang, Hang Su, Jun Zhu:
A Comprehensive Survey of Continual Learning: Theory, Method and Application. CoRR abs/2302.00487 (2023) - [i227]Chenyu Zheng, Guoqiang Wu, Fan Bao, Yue Cao, Chongxuan Li, Jun Zhu:
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications. CoRR abs/2302.02334 (2023) - [i226]Peng Cui, Yang Yue, Zhijie Deng, Jun Zhu:
Confidence-based Reliable Learning under Dual Noises. CoRR abs/2302.05098 (2023) - [i225]Zebin You, Yong Zhong, Fan Bao, Jiacheng Sun, Chongxuan Li, Jun Zhu:
Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels. CoRR abs/2302.10586 (2023) - [i224]Chang Liu, Yinpeng Dong, Wenzhao Xiang, Xiao Yang, Hang Su, Jun Zhu, Yuefeng Chen, Yuan He, Hui Xue, Shibao Zheng:
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking. CoRR abs/2302.14301 (2023) - [i223]Zhongkai Hao, Chengyang Ying, Zhengyi Wang, Hang Su, Yinpeng Dong, Songming Liu, Ze Cheng, Jun Zhu, Jian Song:
GNOT: A General Neural Operator Transformer for Operator Learning. CoRR abs/2302.14376 (2023) - [i222]Yichi Zhang, Zijian Zhu, Hang Su, Jun Zhu, Shibao Zheng, Yuan He, Hui Xue:
To Make Yourself Invisible with Adversarial Semantic Contours. CoRR abs/2303.00284 (2023) - [i221]Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Songming Liu, Jialian Li, Dong Yan, Jun Zhu:
Reward Informed Dreamer for Task Generalization in Reinforcement Learning. CoRR abs/2303.05092 (2023) - [i220]Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang:
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection. CoRR abs/2303.05499 (2023) - [i219]Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu:
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale. CoRR abs/2303.06555 (2023) - [i218]Huanran Chen, Yichi Zhang, Yinpeng Dong, Jun Zhu:
Rethinking Model Ensemble in Transfer-based Adversarial Attacks. CoRR abs/2303.09105 (2023) - [i217]Yinpeng Dong, Caixin Kang, Jinlai Zhang, Zijian Zhu, Yikai Wang, Xiao Yang, Hang Su, Xingxing Wei, Jun Zhu:
Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving. CoRR abs/2303.11040 (2023) - [i216]Xiao Yang, Chang Liu, Longlong Xu, Yikai Wang, Yinpeng Dong, Ning Chen, Hang Su, Jun Zhu:
Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition. CoRR abs/2303.15818 (2023) - [i215]Chendong Xiang, Fan Bao, Chongxuan Li, Hang Su, Jun Zhu:
A Closer Look at Parameter-Efficient Tuning in Diffusion Models. CoRR abs/2303.18181 (2023) - [i214]Shilong Liu, Tianhe Ren, Jiayu Chen, Zhaoyang Zeng, Hao Zhang, Feng Li, Hongyang Li, Jun Huang, Hang Su, Jun Zhu, Lei Zhang:
Detection Transformer with Stable Matching. CoRR abs/2304.04742 (2023) - [i213]Peng Cui, Dan Zhang, Zhijie Deng, Yinpeng Dong, Jun Zhu:
Learning Sample Difficulty from Pre-trained Models for Reliable Prediction. CoRR abs/2304.10127 (2023) - [i212]Jianhui Li, Jianmin Li, Haoji Zhang, Shilong Liu, Zhengyi Wang, Zihao Xiao, Kaiwen Zheng, Jun Zhu:
PREIM3D: 3D Consistent Precise Image Attribute Editing from a Single Image. CoRR abs/2304.10263 (2023) - [i211]Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, Jun Zhu:
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning. CoRR abs/2304.12824 (2023) - [i210]Kaiwen Zheng, Cheng Lu, Jianfei Chen, Jun Zhu:
Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs. CoRR abs/2305.03935 (2023) - [i209]Huanran Chen, Yinpeng Dong, Zhengyi Wang, Xiao Yang, Chengqi Duan, Hang Su, Jun Zhu:
Robust Classification via a Single Diffusion Model. CoRR abs/2305.15241 (2023) - [i208]Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, Jun Zhu:
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation. CoRR abs/2305.16213 (2023) - [i207]Min Zhao, Rongzhen Wang, Fan Bao, Chongxuan Li, Jun Zhu:
ControlVideo: Adding Conditional Control for One Shot Text-to-Video Editing. CoRR abs/2305.17098 (2023) - [i206]Zhanhao Hu, Jun Zhu, Bo Zhang, Xiaolin Hu:
Amplification trojan network: Attack deep neural networks by amplifying their inherent weakness. CoRR abs/2305.17688 (2023) - [i205]Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Ze Cheng, Jun Zhu:
NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data. CoRR abs/2305.18694 (2023) - [i204]Jiachen Yao, Chang Su, Zhongkai Hao, Songming Liu, Hang Su, Jun Zhu:
MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks. CoRR abs/2306.02816 (2023) - [i203]Zhongkai Hao, Jiachen Yao, Chang Su, Hang Su, Ziao Wang, Fanzhi Lu, Zeyu Xia, Yichi Zhang, Songming Liu, Lu Lu, Jun Zhu:
PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs. CoRR abs/2306.08827 (2023) - [i202]Zhijie Deng, Peng Cui, Jun Zhu:
Towards Accelerated Model Training via Bayesian Data Selection. CoRR abs/2308.10544 (2023) - [i201]Feng Zhou, Quyu Kong, Zhijie Deng, Fengxiang He, Peng Cui, Jun Zhu:
Heterogeneous Multi-Task Gaussian Cox Processes. CoRR abs/2308.15364 (2023) - [i200]Yinpeng Dong, Huanran Chen, Jiawei Chen, Zhengwei Fang, Xiao Yang, Yichi Zhang, Yu Tian, Hang Su, Jun Zhu:
How Robust is Google's Bard to Adversarial Image Attacks? CoRR abs/2309.11751 (2023) - [i199]Kaiwen Zheng, Cheng Lu, Jianfei Chen, Jun Zhu:
DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statistics. CoRR abs/2310.13268 (2023) - [i198]Jianhui Li, Shilong Liu, Zidong Liu, Yikai Wang, Kaiwen Zheng, Jinghui Xu, Jianmin Li, Jun Zhu:
InstructPix2NeRF: Instructed 3D Portrait Editing from a Single Image. CoRR abs/2311.02826 (2023) - [i197]Shilong Liu, Hao Cheng, Haotian Liu, Hao Zhang, Feng Li, Tianhe Ren, Xueyan Zou, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang, Jianfeng Gao, Chunyuan Li:
LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents. CoRR abs/2311.05437 (2023) - [i196]Zehua Chen, Guande He, Kaiwen Zheng, Xu Tan, Jun Zhu:
Schrodinger Bridges Beat Diffusion Models on Text-to-Speech Synthesis. CoRR abs/2312.03491 (2023) - 2022
- [i195]Fan Bao, Chongxuan Li, Jun Zhu, Bo Zhang:
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models. CoRR abs/2201.06503 (2022) - [i194]Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, Lei Zhang:
DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR. CoRR abs/2201.12329 (2022) - [i193]Liyuan Wang, Xingxing Zhang, Kuo Yang, Longhui Yu, Chongxuan Li, Lanqing Hong, Shifeng Zhang, Zhenguo Li, Yi Zhong, Jun Zhu:
Memory Replay with Data Compression for Continual Learning. CoRR abs/2202.06592 (2022) - [i192]Tianyu Pang, Min Lin, Xiao Yang, Jun Zhu, Shuicheng Yan:
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition. CoRR abs/2202.10103 (2022) - [i191]Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M. Ni, Heung-Yeung Shum:
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection. CoRR abs/2203.03605 (2022) - [i190]Xiao Yang, Yinpeng Dong, Tianyu Pang, Zihao Xiao, Hang Su, Jun Zhu:
Controllable Evaluation and Generation of Physical Adversarial Patch on Face Recognition. CoRR abs/2203.04623 (2022) - [i189]Yinpeng Dong, Shuyu Cheng, Tianyu Pang, Hang Su, Jun Zhu:
Query-Efficient Black-box Adversarial Attacks Guided by a Transfer-based Prior. CoRR abs/2203.06560 (2022) - [i188]Jialian Li, Tongzheng Ren, Dong Yan, Hang Su, Jun Zhu:
Policy Learning for Robust Markov Decision Process with a Mismatched Generative Model. CoRR abs/2203.06587 (2022) - [i187]Zhijie Deng, Feng Zhou, Jianfei Chen, Guoqiang Wu, Jun Zhu:
Deep Ensemble as a Gaussian Process Approximate Posterior. CoRR abs/2205.00163 (2022) - [i186]Zhijie Deng, Jiaxin Shi, Jun Zhu:
NeuralEF: Deconstructing Kernels by Deep Neural Networks. CoRR abs/2205.00165 (2022) - [i185]Ziyu Wang, Yuhao Zhou, Jun Zhu:
Fast Instrument Learning with Faster Rates. CoRR abs/2205.10772 (2022) - [i184]Shih-Han Chan, Yinpeng Dong, Jun Zhu, Xiaolu Zhang, Jun Zhou:
BadDet: Backdoor Attacks on Object Detection. CoRR abs/2205.14497 (2022) - [i183]Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu:
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps. CoRR abs/2206.00927 (2022) - [i182]Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jun Zhu, Jian Song:
GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing. CoRR abs/2206.04310 (2022) - [i181]Weikai Yang, Xi Ye, Xingxing Zhang, Lanxi Xiao, Jiazhi Xia, Zhongyuan Wang, Jun Zhu, Hanspeter Pfister, Shixia Liu:
Diagnosing Ensemble Few-Shot Classifiers. CoRR abs/2206.04372 (2022) - [i180]Chengyang Ying, Xinning Zhou, Hang Su, Dong Yan, Ning Chen, Jun Zhu:
Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk. CoRR abs/2206.04436 (2022) - [i179]You Qiaoben, Chengyang Ying, Xinning Zhou, Hang Su, Jun Zhu, Bo Zhang:
Consistent Attack: Universal Adversarial Perturbation on Embodied Vision Navigation. CoRR abs/2206.05751 (2022) - [i178]Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu, Bo Zhang:
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models. CoRR abs/2206.07309 (2022) - [i177]Cheng Lu, Kaiwen Zheng, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu:
Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score Matching. CoRR abs/2206.08265 (2022) - [i176]Siyu Wang, Jianfei Chen, Chongxuan Li, Jun Zhu, Bo Zhang:
Fast Lossless Neural Compression with Integer-Only Discrete Flows. CoRR abs/2206.08869 (2022) - [i175]Wenze Chen, Shiyu Huang, Yuan Chiang, Ting Chen, Jun Zhu:
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization. CoRR abs/2207.05631 (2022) - [i174]Liyuan Wang, Xingxing Zhang, Qian Li, Jun Zhu, Yi Zhong:
CoSCL: Cooperation of Small Continual Learners is Stronger than a Big One. CoRR abs/2207.06543 (2022) - [i173]Min Zhao, Fan Bao, Chongxuan Li, Jun Zhu:
EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations. CoRR abs/2207.06635 (2022) - [i172]Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Dong Yan, Jun Zhu:
On the Reuse Bias in Off-Policy Reinforcement Learning. CoRR abs/2209.07074 (2022) - [i171]Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Jian Song, Ze Cheng:
Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients. CoRR abs/2209.07075 (2022) - [i170]Fan Bao, Chongxuan Li, Yue Cao, Jun Zhu:
All are Worth Words: a ViT Backbone for Score-based Diffusion Models. CoRR abs/2209.12152 (2022) - [i169]Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su, Jun Zhu:
Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling. CoRR abs/2209.14548 (2022) - [i168]Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li, Jun Zhu:
Equivariant Energy-Guided SDE for Inverse Molecular Design. CoRR abs/2209.15408 (2022) - [i167]Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Ze Cheng:
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs. CoRR abs/2210.03526 (2022) - [i166]Yinpeng Dong, Shouwei Ruan, Hang Su, Caixin Kang, Xingxing Wei, Jun Zhu:
ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints. CoRR abs/2210.03895 (2022) - [i165]Zhijie Deng, Jiaxin Shi, Hao Zhang, Peng Cui, Cewu Lu, Jun Zhu:
Neural Eigenfunctions Are Structured Representation Learners. CoRR abs/2210.12637 (2022) - [i164]Zhijie Deng, Feng Zhou, Jun Zhu:
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning. CoRR abs/2210.12642 (2022) - [i163]Yibo Miao, Yinpeng Dong, Jun Zhu, Xiao-Shan Gao:
Isometric 3D Adversarial Examples in the Physical World. CoRR abs/2210.15291 (2022) - [i162]Ziyu Wang, Yucen Luo, Yueru Li, Jun Zhu, Bernhard Schölkopf:
Spectral Representation Learning for Conditional Moment Models. CoRR abs/2210.16525 (2022) - [i161]Yao Feng, Yuhong Jiang, Hang Su, Dong Yan, Jun Zhu:
Model-based Reinforcement Learning with a Hamiltonian Canonical ODE Network. CoRR abs/2211.00942 (2022) - [i160]Jinali Zhang, Yinpeng Dong, Jun Zhu, Jihong Zhu, Minchi Kuang, Xiaming Yuan:
Improving transferability of 3D adversarial attacks with scale and shear transformations. CoRR abs/2211.01093 (2022) - [i159]Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu:
DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models. CoRR abs/2211.01095 (2022) - [i158]Zhongkai Hao, Songming Liu, Yichi Zhang, Chengyang Ying, Yao Feng, Hang Su, Jun Zhu:
Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications. CoRR abs/2211.08064 (2022) - [i157]Shilong Liu, Yaoyuan Liang, Feng Li, Shijia Huang, Hao Zhang, Hang Su, Jun Zhu, Lei Zhang:
DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding. CoRR abs/2211.15516 (2022) - [i156]Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu:
Why Are Conditional Generative Models Better Than Unconditional Ones? CoRR abs/2212.00362 (2022) - 2021
- [i155]Liyuan Wang, Kuo Yang, Chongxuan Li, Lanqing Hong, Zhenguo Li, Jun Zhu:
ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning. CoRR abs/2101.00407 (2021) - [i154]Qijun Luo, Zhili Liu, Lanqing Hong, Chongxuan Li, Kuo Yang, Liyuan Wang, Fengwei Zhou, Guilin Li, Zhenguo Li, Jun Zhu:
Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptation. CoRR abs/2101.01400 (2021) - [i153]Xiao Li, Jianmin Li, Ting Dai, Jie Shi, Jun Zhu, Xiaolin Hu:
Rethinking Natural Adversarial Examples for Classification Models. CoRR abs/2102.11731 (2021) - [i152]Qiang Liu, Zhaocheng Liu, Haoli Zhang, Yuntian Chen, Jun Zhu:
DNN2LR: Automatic Feature Crossing for Credit Scoring. CoRR abs/2102.12036 (2021) - [i151]Cheng Lu, Jianfei Chen, Chongxuan Li, Qiuhao Wang, Jun Zhu:
Implicit Normalizing Flows. CoRR abs/2103.09527 (2021) - [i150]Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, Jun Zhu:
Black-box Detection of Backdoor Attacks with Limited Information and Data. CoRR abs/2103.13127 (2021) - [i149]Zhijie Deng, Xiao Yang, Shizhen Xu, Hang Su, Jun Zhu:
LiBRe: A Practical Bayesian Approach to Adversarial Detection. CoRR abs/2103.14835 (2021) - [i148]Peng Cui, Zhijie Deng, Wenbo Hu, Jun Zhu:
Accurate and Reliable Forecasting using Stochastic Differential Equations. CoRR abs/2103.15041 (2021) - [i147]Liyuan Wang, Qian Li, Yi Zhong, Jun Zhu:
Few-shot Continual Learning: a Brain-inspired Approach. CoRR abs/2104.09034 (2021) - [i146]Tsung Wei Tsai, Chongxuan Li, Jun Zhu:
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering. CoRR abs/2105.01899 (2021) - [i145]Qi-An Fu, Yinpeng Dong, Hang Su, Jun Zhu:
Automated Decision-based Adversarial Attacks. CoRR abs/2105.03931 (2021) - [i144]Guoqiang Wu, Chongxuan Li, Kun Xu, Jun Zhu:
Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization. CoRR abs/2105.05026 (2021) - [i143]Shilong Liu, Lei Zhang, Xiao Yang, Hang Su, Jun Zhu:
Unsupervised Part Segmentation through Disentangling Appearance and Shape. CoRR abs/2105.12405 (2021) - [i142]Tianyu Pang, Huishuai Zhang, Di He, Yinpeng Dong, Hang Su, Wei Chen, Jun Zhu, Tie-Yan Liu:
Adversarial Training with Rectified Rejection. CoRR abs/2105.14785 (2021) - [i141]Yingtao Luo, Qiang Liu, Yuntian Chen, Wenbo Hu, Jun Zhu:
KO-PDE: Kernel Optimized Discovery of Partial Differential Equations with Varying Coefficients. CoRR abs/2106.01078 (2021) - [i140]Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, Jun Zhu:
Exploring Memorization in Adversarial Training. CoRR abs/2106.01606 (2021) - [i139]Fan Bao, Guoqiang Wu, Chongxuan Li, Jun Zhu, Bo Zhang:
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization. CoRR abs/2106.04188 (2021) - [i138]Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Liang Zhang, Wentao Han, Minlie Huang, Qin Jin, Yanyan Lan, Yang Liu, Zhiyuan Liu, Zhiwu Lu, Xipeng Qiu, Ruihua Song, Jie Tang, Ji-Rong Wen, Jinhui Yuan, Wayne Xin Zhao, Jun Zhu:
Pre-Trained Models: Past, Present and Future. CoRR abs/2106.07139 (2021) - [i137]Ziyu Wang, Yuhao Zhou, Tongzheng Ren, Jun Zhu:
Scalable Quasi-Bayesian Inference for Instrumental Variable Regression. CoRR abs/2106.08750 (2021) - [i136]Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu:
Accumulative Poisoning Attacks on Real-time Data. CoRR abs/2106.09993 (2021) - [i135]Zihao Xiao, Xianfeng Gao, Chilin Fu, Yinpeng Dong, Wei Gao, Xiaolu Zhang, Jun Zhou, Jun Zhu:
Improving Transferability of Adversarial Patches on Face Recognition with Generative Models. CoRR abs/2106.15058 (2021) - [i134]Yichi Zhou, Shihong Song, Huishuai Zhang, Jun Zhu, Wei Chen, Tie-Yan Liu:
Regularized OFU: an Efficient UCB Estimator forNon-linear Contextual Bandit. CoRR abs/2106.15128 (2021) - [i133]You Qiaoben, Chengyang Ying, Xinning Zhou, Hang Su, Jun Zhu, Bo Zhang:
Understanding Adversarial Attacks on Observations in Deep Reinforcement Learning. CoRR abs/2106.15860 (2021) - [i132]Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu:
Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks. CoRR abs/2107.01809 (2021) - [i131]Shuyu Cheng, Guoqiang Wu, Jun Zhu:
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms. CoRR abs/2107.10110 (2021) - [i130]Shilong Liu, Lei Zhang, Xiao Yang, Hang Su, Jun Zhu:
Query2Label: A Simple Transformer Way to Multi-Label Classification. CoRR abs/2107.10834 (2021) - [i129]Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Hang Su, Jun Zhu:
Tianshou: a Highly Modularized Deep Reinforcement Learning Library. CoRR abs/2107.14171 (2021) - [i128]Zhengyi Wang, Zhongkai Hao, Hang Su, Jun Zhu:
Query-based Adversarial Attacks on Graph with Fake Nodes. CoRR abs/2109.13069 (2021) - [i127]Yichi Zhang, Zijian Zhu, Xiao Yang, Jun Zhu:
Adversarial Semantic Contour for Object Detection. CoRR abs/2109.15009 (2021) - [i126]Shiyu Huang, Bin Wang, Dong Li, Jianye Hao, Ting Chen, Jun Zhu:
Ranking Cost: Building An Efficient and Scalable Circuit Routing Planner with Evolution-Based Optimization. CoRR abs/2110.03939 (2021) - [i125]Shiyu Huang, Wenze Chen, Longfei Zhang, Ziyang Li, Fengming Zhu, Deheng Ye, Ting Chen, Jun Zhu:
TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations. CoRR abs/2110.04507 (2021) - [i124]Yinpeng Dong, Qi-An Fu, Xiao Yang, Wenzhao Xiang, Tianyu Pang, Hang Su, Jun Zhu, Jiayu Tang, Yuefeng Chen, Xiaofeng Mao, Yuan He, Hui Xue, Chao Li, Ye Liu, Qilong Zhang, Lianli Gao, Yunrui Yu, Xitong Gao, Zhe Zhao, Daquan Lin, Jiadong Lin, Chuanbiao Song, Zihao Wang, Zhennan Wu, Yang Guo, Jiequan Cui, Xiaogang Xu, Pengguang Chen:
Adversarial Attacks on ML Defense Models Competition. CoRR abs/2110.08042 (2021) - [i123]Xiao Yang, Yinpeng Dong, Wenzhao Xiang, Tianyu Pang, Hang Su, Jun Zhu:
Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness. CoRR abs/2110.08256 (2021) - [i122]Yuefeng Chen, Xiaofeng Mao, Yuan He, Hui Xue, Chao Li, Yinpeng Dong, Qi-An Fu, Xiao Yang, Wenzhao Xiang, Tianyu Pang, Hang Su, Jun Zhu, Fangcheng Liu, Chao Zhang, Hongyang Zhang, Yichi Zhang, Shilong Liu, Chang Liu, Wenzhao Xiang, Yajie Wang, Huipeng Zhou, Haoran Lyu, Yidan Xu, Zixuan Xu, Taoyu Zhu, Wenjun Li, Xianfeng Gao, Guoqiu Wang, Huanqian Yan, Ying Guo, Chaoning Zhang, Zheng Fang, Yang Wang, Bingyang Fu, Yunfei Zheng, Yekui Wang, Haorong Luo, Zhen Yang:
Unrestricted Adversarial Attacks on ImageNet Competition. CoRR abs/2110.09903 (2021) - [i121]Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, Yi Zhong:
AFEC: Active Forgetting of Negative Transfer in Continual Learning. CoRR abs/2110.12187 (2021) - 2020
- [i120]Kelei Cao, Mengchen Liu, Hang Su, Jing Wu, Jun Zhu, Shixia Liu:
Analyzing the Noise Robustness of Deep Neural Networks. CoRR abs/2001.09395 (2020) - [i119]Zhijie Deng, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu:
Adversarial Distributional Training for Robust Deep Learning. CoRR abs/2002.05999 (2020) - [i118]Ziyu Wang, Shuyu Cheng, Yueru Li, Jun Zhu, Bo Zhang:
A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models. CoRR abs/2002.07501 (2020) - [i117]Tianyu Pang, Xiao Yang, Yinpeng Dong, Kun Xu, Hang Su, Jun Zhu:
Boosting Adversarial Training with Hypersphere Embedding. CoRR abs/2002.08619 (2020) - [i116]Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, Tian Tian:
VFlow: More Expressive Generative Flows with Variational Data Augmentation. CoRR abs/2002.09741 (2020) - [i115]Liyuan Wang, Bo Lei, Qian Li, Hang Su, Jun Zhu, Yi Zhong:
Triple Memory Networks: a Brain-Inspired Method for Continual Learning. CoRR abs/2003.03143 (2020) - [i114]Xiao Yang, Yinpeng Dong, Tianyu Pang, Jun Zhu, Hang Su:
Towards Privacy Protection by Generating Adversarial Identity Masks. CoRR abs/2003.06814 (2020) - [i113]Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen:
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. CoRR abs/2004.00353 (2020) - [i112]Yuhao Zhou, Jiaxin Shi, Jun Zhu:
Nonparametric Score Estimators. CoRR abs/2005.10099 (2020) - [i111]Yujie Wu, Rong Zhao, Jun Zhu, Feng Chen, Mingkun Xu, Guoqi Li, Sen Song, Lei Deng, Guanrui Wang, Hao Zheng, Jing Pei, Youhui Zhang, Mingguo Zhao, Luping Shi:
Brain-inspired global-local hybrid learning towards human-like intelligence. CoRR abs/2006.03226 (2020) - [i110]Zhiheng Zhang, Wenbo Hu, Tian Tian, Jun Zhu:
Dynamic Window-level Granger Causality of Multi-channel Time Series. CoRR abs/2006.07788 (2020) - [i109]Peng Cui, Wenbo Hu, Jun Zhu:
Calibrated Reliable Regression using Maximum Mean Discrepancy. CoRR abs/2006.10255 (2020) - [i108]Tianyu Pang, Kun Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu:
Efficient Learning of Generative Models via Finite-Difference Score Matching. CoRR abs/2007.03317 (2020) - [i107]Xiao Yang, Dingcheng Yang, Yinpeng Dong, Wenjian Yu, Hang Su, Jun Zhu:
Delving into the Adversarial Robustness on Face Recognition. CoRR abs/2007.04118 (2020) - [i106]Haoyu Liang, Zhihao Ouyang, Yuyuan Zeng, Hang Su, Zihao He, Shu-Tao Xia, Jun Zhu, Bo Zhang:
Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters. CoRR abs/2007.08194 (2020) - [i105]Qiang Liu, Zhaocheng Liu, Xiaofang Zhu, Yeliang Xiu, Jun Zhu:
Deep Active Learning by Model Interpretability. CoRR abs/2007.12100 (2020) - [i104]Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu:
Bag of Tricks for Adversarial Training. CoRR abs/2010.00467 (2020) - [i103]Zhijie Deng, Xiao Yang, Hao Zhang, Yinpeng Dong, Jun Zhu:
BayesAdapter: Being Bayesian, Inexpensively and Robustly, via Bayeisan Fine-tuning. CoRR abs/2010.01979 (2020) - [i102]Fan Bao, Chongxuan Li, Kun Xu, Hang Su, Jun Zhu, Bo Zhang:
Bi-level Score Matching for Learning Energy-based Latent Variable Models. CoRR abs/2010.07856 (2020) - [i101]Fan Bao, Kun Xu, Chongxuan Li, Lanqing Hong, Jun Zhu, Bo Zhang:
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models. CoRR abs/2010.08258 (2020) - [i100]Ziyu Wang, Bin Dai, David P. Wipf, Jun Zhu:
Further Analysis of Outlier Detection with Deep Generative Models. CoRR abs/2010.13064 (2020) - [i99]Guoqiang Wu, Jun Zhu:
Multi-label classification: do Hamming loss and subset accuracy really conflict with each other? CoRR abs/2011.07805 (2020) - [i98]Qipeng Guo, Zhijing Jin, Ziyu Wang, Xipeng Qiu, Weinan Zhang, Jun Zhu, Zheng Zhang, David Wipf:
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings. CoRR abs/2012.07412 (2020) - [i97]Qingyi Pan, Wenbo Hu, Jun Zhu:
Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting. CoRR abs/2012.09324 (2020) - 2019
- [i96]Chongxuan Li, Chao Du, Kun Xu, Max Welling, Jun Zhu, Bo Zhang:
Adversarial Variational Inference and Learning in Markov Random Fields. CoRR abs/1901.08400 (2019) - [i95]Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu:
Improving Adversarial Robustness via Promoting Ensemble Diversity. CoRR abs/1901.08846 (2019) - [i94]Yinpeng Dong, Fan Bao, Hang Su, Jun Zhu:
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples. CoRR abs/1901.09035 (2019) - [i93]Haosheng Zou, Tongzheng Ren, Dong Yan, Hang Su, Jun Zhu:
Reward Shaping via Meta-Learning. CoRR abs/1901.09330 (2019) - [i92]Chang Liu, Jingwei Zhuo, Jun Zhu:
Understanding MCMC Dynamics as Flows on the Wasserstein Space. CoRR abs/1902.00282 (2019) - [i91]Zhijie Deng, Yinpeng Dong, Jun Zhu:
Batch Virtual Adversarial Training for Graph Convolutional Networks. CoRR abs/1902.09192 (2019) - [i90]Ziyu Wang, Tongzheng Ren, Jun Zhu, Bo Zhang:
Function Space Particle Optimization for Bayesian Neural Networks. CoRR abs/1902.09754 (2019) - [i89]Zhijie Deng, Yucen Luo, Jun Zhu:
Cluster Alignment with a Teacher for Unsupervised Domain Adaptation. CoRR abs/1903.09980 (2019) - [i88]Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu:
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks. CoRR abs/1904.02884 (2019) - [i87]Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, Jun Zhu:
Efficient Decision-based Black-box Adversarial Attacks on Face Recognition. CoRR abs/1904.04433 (2019) - [i86]Fan Bao, Hang Su, Jun Zhu:
Boosting Generative Models by Leveraging Cascaded Meta-Models. CoRR abs/1905.04534 (2019) - [i85]Tianyu Pang, Kun Xu, Yinpeng Dong, Chao Du, Ning Chen, Jun Zhu:
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness. CoRR abs/1905.10626 (2019) - [i84]Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu:
Scalable Training of Inference Networks for Gaussian-Process Models. CoRR abs/1905.10969 (2019) - [i83]Tsung Wei Tsai, Chongxuan Li, Jun Zhu:
DS3L: Deep Self-Semi-Supervised Learning for Image Recognition. CoRR abs/1905.13305 (2019) - [i82]Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang:
Multi-objects Generation with Amortized Structural Regularization. CoRR abs/1906.03923 (2019) - [i81]Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu:
Improving Black-box Adversarial Attacks with a Transfer-based Prior. CoRR abs/1906.06919 (2019) - [i80]Yucen Luo, Jun Zhu, Tomas Pfister:
A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels. CoRR abs/1909.09338 (2019) - [i79]Tianyu Pang, Kun Xu, Jun Zhu:
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks. CoRR abs/1909.11515 (2019) - [i78]Zheyu Yang, Yujie Wu, Guanrui Wang, Yukuan Yang, Guoqi Li, Lei Deng, Jun Zhu, Luping Shi:
DashNet: A Hybrid Artificial and Spiking Neural Network for High-speed Object Tracking. CoRR abs/1909.12942 (2019) - [i77]Kun Xu, Chongxuan Li, Huanshu Wei, Jun Zhu, Bo Zhang:
Understanding and Stabilizing GANs' Training Dynamics with Control Theory. CoRR abs/1909.13188 (2019) - [i76]Zhijie Deng, Yucen Luo, Jun Zhu, Bo Zhang:
DBSN: Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structures. CoRR abs/1911.09804 (2019) - [i75]Xiao Yang, Fangyun Wei, Hongyang Zhang, Xiang Ming, Jun Zhu:
Design and Interpretation of Universal Adversarial Patches in Face Detection. CoRR abs/1912.05021 (2019) - [i74]Chongxuan Li, Kun Xu, Jiashuo Liu, Jun Zhu, Bo Zhang:
Triple Generative Adversarial Networks. CoRR abs/1912.09784 (2019) - [i73]Yinpeng Dong, Qi-An Fu, Xiao Yang, Tianyu Pang, Hang Su, Zihao Xiao, Jun Zhu:
Benchmarking Adversarial Robustness. CoRR abs/1912.11852 (2019) - 2018
- [i72]Haosheng Zou, Hang Su, Shihong Song, Jun Zhu:
Understanding Human Behaviors in Crowds by Imitating the Decision-Making Process. CoRR abs/1801.08391 (2018) - [i71]Tianyu Pang, Chao Du, Jun Zhu:
Max-Mahalanobis Linear Discriminant Analysis Networks. CoRR abs/1802.09308 (2018) - [i70]Xingxing Wei, Jun Zhu, Hang Su:
Sparse Adversarial Perturbations for Videos. CoRR abs/1803.02536 (2018) - [i69]Alexey Kurakin, Ian J. Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, Alan L. Yuille, Sangxia Huang, Yao Zhao, Yuzhe Zhao, Zhonglin Han, Junjiajia Long, Yerkebulan Berdibekov, Takuya Akiba, Seiya Tokui, Motoki Abe:
Adversarial Attacks and Defences Competition. CoRR abs/1804.00097 (2018) - [i68]Chongxuan Li, Max Welling, Jun Zhu, Bo Zhang:
Graphical Generative Adversarial Networks. CoRR abs/1804.03429 (2018) - [i67]Zihao Xiao, Jianfei Chen, Jun Zhu:
Towards Training Probabilistic Topic Models on Neuromorphic Multi-chip Systems. CoRR abs/1804.03578 (2018) - [i66]Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song:
Adversarial Attack on Graph Structured Data. CoRR abs/1806.02371 (2018) - [i65]Jiaxin Shi, Shengyang Sun, Jun Zhu:
A Spectral Approach to Gradient Estimation for Implicit Distributions. CoRR abs/1806.02925 (2018) - [i64]Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu, Lawrence Carin:
Accelerated First-order Methods on the Wasserstein Space for Bayesian Inference. CoRR abs/1807.01750 (2018) - [i63]Chao Du, Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang:
Learning Implicit Generative Models by Teaching Explicit Ones. CoRR abs/1807.03870 (2018) - [i62]Kun Xu, Haoyu Liang, Jun Zhu, Hang Su, Bo Zhang:
Deep Structured Generative Models. CoRR abs/1807.03877 (2018) - [i61]Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Luping Shi:
Direct Training for Spiking Neural Networks: Faster, Larger, Better. CoRR abs/1809.05793 (2018) - [i60]Mengchen Liu, Shixia Liu, Hang Su, Kelei Cao, Jun Zhu:
Analyzing the Noise Robustness of Deep Neural Networks. CoRR abs/1810.03913 (2018) - [i59]Yichi Zhou, Tongzheng Ren, Jialian Li, Dong Yan, Jun Zhu:
Lazy-CFR: a fast regret minimization algorithm for extensive games with imperfect information. CoRR abs/1810.04433 (2018) - [i58]Yucen Luo, Tian Tian, Jiaxin Shi, Jun Zhu, Bo Zhang:
Semi-crowdsourced Clustering with Deep Generative Models. CoRR abs/1810.11971 (2018) - [i57]You Qiaoben, Zheng Wang, Jianguo Li, Yinpeng Dong, Yu-Gang Jiang, Jun Zhu:
Composite Binary Decomposition Networks. CoRR abs/1811.06668 (2018) - 2017
- [i56]Shixia Liu, Xiting Wang, Mengchen Liu, Jun Zhu:
Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective. CoRR abs/1702.01226 (2017) - [i55]Jianfei Chen, Jun Zhu, Jie Lu, Shixia Liu:
Scalable Inference for Nested Chinese Restaurant Process Topic Models. CoRR abs/1702.07083 (2017) - [i54]Chongxuan Li, Kun Xu, Jun Zhu, Bo Zhang:
Triple Generative Adversarial Nets. CoRR abs/1703.02291 (2017) - [i53]Yinpeng Dong, Hang Su, Jun Zhu, Bo Zhang:
Improving Interpretability of Deep Neural Networks with Semantic Information. CoRR abs/1703.04096 (2017) - [i52]Jiaxin Shi, Shengyang Sun, Jun Zhu:
Implicit Variational Inference with Kernel Density Ratio Fitting. CoRR abs/1705.10119 (2017) - [i51]Tianyu Pang, Chao Du, Jun Zhu:
Robust Deep Learning via Reverse Cross-Entropy Training and Thresholding Test. CoRR abs/1706.00633 (2017) - [i50]Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Luping Shi:
Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks. CoRR abs/1706.02609 (2017) - [i49]Wenbo Hu, Lifeng Hua, Lei Li, Tian Wang, Jun Zhu, Hang Su, Bo Zhang:
SAM: Semantic Attribute Modulated Language Modeling. CoRR abs/1707.00117 (2017) - [i48]Yinpeng Dong, Renkun Ni, Jianguo Li, Yurong Chen, Jun Zhu, Hang Su:
Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization. CoRR abs/1708.01001 (2017) - [i47]Yichi Zhou, Jun Zhu, Jingwei Zhuo:
Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors. CoRR abs/1708.04781 (2017) - [i46]Yinpeng Dong, Hang Su, Jun Zhu, Fan Bao:
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples. CoRR abs/1708.05493 (2017) - [i45]Jiaxin Shi, Jianfei Chen, Jun Zhu, Shengyang Sun, Yucen Luo, Yihong Gu, Yuhao Zhou:
ZhuSuan: A Library for Bayesian Deep Learning. CoRR abs/1709.05870 (2017) - [i44]Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Xiaolin Hu, Jun Zhu:
Discovering Adversarial Examples with Momentum. CoRR abs/1710.06081 (2017) - [i43]Jianfei Chen, Jun Zhu:
Stochastic Training of Graph Convolutional Networks. CoRR abs/1710.10568 (2017) - [i42]Yucen Luo, Jun Zhu, Mengxi Li, Yong Ren, Bo Zhang:
Smooth Neighbors on Teacher Graphs for Semi-supervised Learning. CoRR abs/1711.00258 (2017) - [i41]Zhijie Deng, Hao Zhang, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu, Eric P. Xing:
Structured Generative Adversarial Networks. CoRR abs/1711.00889 (2017) - [i40]Pengtao Xie, Jun Zhu, Eric P. Xing:
Diversity-Promoting Bayesian Learning of Latent Variable Models. CoRR abs/1711.08770 (2017) - [i39]Jianqiao Wangni, Jingwei Zhuo, Jun Zhu:
Learning Random Fourier Features by Hybrid Constrained Optimization. CoRR abs/1712.02527 (2017) - [i38]Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Jun Zhu, Xiaolin Hu:
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser. CoRR abs/1712.02976 (2017) - [i37]Jian Wu, Changran Hu, Yulong Wang, Xiaolin Hu, Jun Zhu:
A Hierarchical Recurrent Neural Network for Symbolic Melody Generation. CoRR abs/1712.05274 (2017) - [i36]Danyang Sun, Tongzheng Ren, Chongxuan Li, Jun Zhu, Hang Su:
Learning to Write Stylized Chinese Characters by Reading a Handful of Examples. CoRR abs/1712.06424 (2017) - 2016
- [i35]Yang Gao, Jianfei Chen, Jun Zhu:
Streaming Gibbs Sampling for LDA Model. CoRR abs/1601.01142 (2016) - [i34]Yong Ren, Yining Wang, Jun Zhu:
Spectral Learning for Supervised Topic Models. CoRR abs/1602.06025 (2016) - [i33]Chongxuan Li, Jun Zhu, Bo Zhang:
Learning to Generate with Memory. CoRR abs/1602.07416 (2016) - [i32]Jun Zhu, Jiaming Song, Bei Chen:
Max-Margin Nonparametric Latent Feature Models for Link Prediction. CoRR abs/1602.07428 (2016) - [i31]Mengchen Liu, Jiaxin Shi, Zhen Li, Chongxuan Li, Jun Zhu, Shixia Liu:
Towards Better Analysis of Deep Convolutional Neural Networks. CoRR abs/1604.07043 (2016) - [i30]Shaohua Li, Tat-Seng Chua, Jun Zhu, Chunyan Miao:
Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs). CoRR abs/1606.02979 (2016) - [i29]Shaohua Li, Jun Zhu, Chunyan Miao:
PSDVec: a Toolbox for Incremental and Scalable Word Embedding. CoRR abs/1606.03192 (2016) - [i28]Yong Ren, Jialian Li, Yucen Luo, Jun Zhu:
Conditional Generative Moment-Matching Networks. CoRR abs/1606.04218 (2016) - [i27]Kaiwei Li, Jianfei Chen, Wenguang Chen, Jun Zhu:
SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs. CoRR abs/1610.02496 (2016) - [i26]Chongxuan Li, Jun Zhu, Bo Zhang:
Max-Margin Deep Generative Models for (Semi-)Supervised Learning. CoRR abs/1611.07119 (2016) - [i25]Jingkang Yang, Haohan Wang, Jun Zhu, Eric P. Xing:
SeDMiD for Confusion Detection: Uncovering Mind State from Time Series Brain Wave Data. CoRR abs/1611.10252 (2016) - [i24]Binghong Chen, Jun Zhu:
A Communication-Efficient Parallel Method for Group-Lasso. CoRR abs/1612.02222 (2016) - [i23]Chao Du, Chongxuan Li, Yin Zheng, Jun Zhu, Cailiang Liu, Hanning Zhou, Bo Zhang:
Collaborative Filtering with User-Item Co-Autoregressive Models. CoRR abs/1612.07146 (2016) - 2015
- [i22]Chongxuan Li, Jun Zhu, Tianlin Shi, Bo Zhang:
Max-margin Deep Generative Models. CoRR abs/1504.06787 (2015) - [i21]Wenbo Hu, Jun Zhu, Bo Zhang:
Stochastic Subgradient MCMC Methods. CoRR abs/1504.07107 (2015) - [i20]Renjie Liao, Jianping Shi, Ziyang Ma, Jun Zhu, Jiaya Jia:
Bounded-Distortion Metric Learning. CoRR abs/1505.02377 (2015) - [i19]Chao Du, Jun Zhu, Bo Zhang:
Learning Deep Generative Models with Doubly Stochastic MCMC. CoRR abs/1506.04557 (2015) - [i18]Ning Chen, Jun Zhu, Jianfei Chen, Ting Chen:
Dropout Training for SVMs with Data Augmentation. CoRR abs/1508.02268 (2015) - [i17]Shaohua Li, Jun Zhu, Chunyan Miao:
A Generative Word Embedding Model and its Low Rank Positive Semidefinite Solution. CoRR abs/1508.03826 (2015) - [i16]Jianfei Chen, Kaiwei Li, Jun Zhu, Wenguang Chen:
WarpLDA: a Simple and Efficient O(1) Algorithm for Latent Dirichlet Allocation. CoRR abs/1510.08628 (2015) - [i15]Yang Song, Jun Zhu:
Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization. CoRR abs/1512.01110 (2015) - [i14]Jiaxin Shi, Jun Zhu:
Building Memory with Concept Learning Capabilities from Large-scale Knowledge Base. CoRR abs/1512.01173 (2015) - [i13]Bei Chen, Jun Zhu, Nan Yang, Tian Tian, Ming Zhou, Bo Zhang:
Jointly Modeling Topics and Intents with Global Order Structure. CoRR abs/1512.02009 (2015) - [i12]Bei Chen, Ning Chen, Jun Zhu, Jiaming Song, Bo Zhang:
Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation. CoRR abs/1512.02016 (2015) - [i11]Hugh Perkins, Minjie Xu, Jun Zhu, Bo Zhang:
Fast Parallel SVM using Data Augmentation. CoRR abs/1512.07716 (2015) - 2014
- [i10]Ning Chen, Jun Zhu, Jianfei Chen, Bo Zhang:
Dropout Training for Support Vector Machines. CoRR abs/1404.4171 (2014) - [i9]Ni Lao, Jun Zhu:
Contrastive Feature Induction for Efficient Structure Learning of Conditional Random Fields. CoRR abs/1406.7445 (2014) - [i8]Jun Zhu, Jianfei Chen, Wenbo Hu:
Big Learning with Bayesian Methods. CoRR abs/1411.6370 (2014) - 2013
- [i7]Jun Zhu, Xun Zheng, Bo Zhang:
Improved Bayesian Logistic Supervised Topic Models with Data Augmentation. CoRR abs/1310.2408 (2013) - [i6]Ning Chen, Jun Zhu, Fei Xia, Bo Zhang:
Discriminative Relational Topic Models. CoRR abs/1310.2409 (2013) - [i5]Jun Zhu, Ning Chen, Hugh Perkins, Bo Zhang:
Gibbs Max-margin Topic Models with Data Augmentation. CoRR abs/1310.2816 (2013) - [i4]Tianlin Shi, Jun Zhu:
Online Bayesian Passive-Aggressive Learning. CoRR abs/1312.3388 (2013) - 2012
- [i3]Jun Zhu, Eric P. Xing:
Sparse Topical Coding. CoRR abs/1202.3778 (2012) - [i2]Jun Zhu:
Max-Margin Nonparametric Latent Feature Models for Link Prediction. CoRR abs/1206.4659 (2012) - [i1]Jun Zhu, Ning Chen, Eric P. Xing:
Bayesian Inference with Posterior Regularization and Infinite Latent Support Vector Machines. CoRR abs/1210.1766 (2012)
Coauthor Index
[j59] [j50] [j46] [j32] [c227] [c226] [c218] [c204] [c202] [c201] [c199] [c194] [c183] [c181] [c179] [c171] [c170] [c169] [c167] [c163] [c155] [c147] [c135] [c134] [c132] [c126] [c123] [c122] [c113] [c106] [c103] [c89] [c83] [c78] [c67] [c64] [i224] [i223] [i218] [i217] [i216] [i213] [i209] [i200] [i190] [i189] [i184] [i182] [i166] [i163] [i160] [i150] [i145] [i142] [i140] [i136] [i135] [i132] [i124] [i123] [i122] [i119] [i117] [i114] [i107] [i104] [i103] [i94] [i91] [i88] [i87] [i85] [i81] [i73] [i69] [i57] [i53] [i48] [i46] [i44] [i38]
[j60] [j54] [j49] [j47] [j26] [j16] [c224] [c222] [c219] [c216] [c210] [c200] [c197] [c196] [c193] [c191] [c187] [c186] [c173] [c168] [c166] [c165] [c162] [c160] [c149] [c142] [c137] [c133] [c130] [c114] [c111] [c93] [c88] [c76] [c75] [c65] [c52] [i227] [i225] [i219] [i215] [i211] [i208] [i207] [i195] [i193] [i183] [i178] [i177] [i176] [i173] [i170] [i168] [i159] [i156] [i155] [i154] [i151] [i146] [i144] [i139] [i108] [i102] [i101] [i96] [i83] [i82] [i77] [i74] [i68] [i63] [i54] [i36] [i33] [i31] [i26] [i23] [i22]
[j50] [c204] [c201] [c199] [c192] [c170] [c169] [c167] [c163] [c155] [c147] [c146] [c134] [c133] [c132] [c123] [c117] [c113] [c106] [c103] [c98] [c89] [i192] [i190] [i189] [i150] [i142] [i140] [i136] [i132] [i124] [i123] [i122] [i119] [i117] [i114] [i108] [i104] [i95] [i88] [i85] [i81] [i79] [i73] [i71] [i69] [i51] [i44] [i38]
[j60] [j58] [j57] [j51] [j50] [j47] [j46] [j45] [j40] [j38] [j36] [j32] [j31] [j17] [j12] [c229] [c227] [c226] [c224] [c223] [c221] [c220] [c219] [c218] [c217] [c216] [c215] [c213] [c210] [c208] [c204] [c201] [c199] [c198] [c194] [c190] [c189] [c183] [c182] [c179] [c177] [c174] [c172] [c170] [c169] [c167] [c164] [c163] [c157] [c155] [c153] [c152] [c150] [c139] [c137] [c134] [c132] [c128] [c125] [c124] [c123] [c122] [c115] [c113] [c107] [c105] [c103] [c101] [c94] [c93] [c91] [c83] [c81] [c78] [c68] [c67] [c64] [i228] [i224] [i223] [i222] [i221] [i220] [i219] [i217] [i216] [i215] [i214] [i211] [i209] [i208] [i205] [i204] [i203] [i200] [i197] [i194] [i191] [i190] [i189] [i188] [i182] [i180] [i179] [i172] [i171] [i169] [i167] [i166] [i161] [i158] [i157] [i150] [i149] [i145] [i143] [i142] [i140] [i136] [i133] [i132] [i130] [i129] [i128] [i124] [i123] [i122] [i120] [i119] [i117] [i115] [i114] [i107] [i106] [i104] [i102] [i94] [i93] [i88] [i87] [i86] [i81] [i73] [i72] [i70] [i62] [i60] [i53] [i49] [i48] [i46] [i36]
[j60] [j58] [j52] [j49] [j26] [j23] [j20] [j14] [j10] [j9] [j7] [j6] [j1] [c200] [c196] [c191] [c165] [c162] [c156] [c153] [c149] [c142] [c139] [c137] [c130] [c120] [c114] [c111] [c105] [c104] [c101] [c95] [c90] [c88] [c83] [c81] [c78] [c76] [c74] [c72] [c70] [c69] [c68] [c67] [c65] [c64] [c58] [c55] [c52] [c47] [c44] [c38] [c36] [c34] [c33] [c32] [c31] [c30] [c29] [c28] [c24] [c9] [c8] [c6] [c5] [c4] [c3] [c2] [c1] [r1] [i206] [i195] [i179] [i178] [i176] [i139] [i133] [i118] [i106] [i102] [i101] [i96] [i90] [i82] [i77] [i76] [i74] [i68] [i63] [i62] [i58] [i54] [i53] [i49] [i42] [i33] [i26] [i23] [i22] [i21] [i19] [i13] [i12] [i11] [i10] [i7] [i6] [i5]
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