Jun Zhu

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Nanjing University of Aeronautics and Astronautics ∙ Qualcomm ∙ University of Wisconsin-Madison ∙ Carnegie Mellon University

Associate Professor, Computer Science Department at Tsinghua University. Adjunct Faculty, Machine Learning Department at Carnegie Mellon University.

Deep Active Learning by Model Interpretability

Recent successes of Deep Neural Networks (DNNs) in a variety of research...
07/23/2020 ∙ by Qiang Liu, et al. ∙ 0 ∙ share

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Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters

Convolutional neural networks (CNNs) have been successfully used in a ra...
07/16/2020 ∙ by Haoyu Liang, et al. ∙ 0 ∙ share

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A quasi-conservative discontinuous Galerkin method for multi-component flows using the non-oscillatory kinetic flux

In this paper, a high order quasi-conservative discontinuous Galerkin (D...
07/12/2020 ∙ by Dongmi Luo, et al. ∙ 0 ∙ share

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Delving into the Adversarial Robustness on Face Recognition

Face recognition has recently made substantial progress and achieved hig...
07/08/2020 ∙ by Xiao Yang, et al. ∙ 10 ∙ share

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Efficient Inference of Nonparametric Interaction in Spiking-neuron Networks

Hawkes process provides an effective statistical framework for analyzing...
06/23/2020 ∙ by Feng Zhou, et al. ∙ 6 ∙ share

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Absolutely convergent fixed-point fast sweeping WENO methods for steady state of hyperbolic conservation laws

Fixed-point iterative sweeping methods were developed in the literature ...
06/21/2020 ∙ by Liang Li, et al. ∙ 0 ∙ share

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Calibrated Reliable Regression using Maximum Mean Discrepancy

Accurate quantification of uncertainty is crucial for real-world applica...
06/18/2020 ∙ by Peng Cui, et al. ∙ 0 ∙ share

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Dynamic Window-level Granger Causality of Multi-channel Time Series

Granger causality method analyzes the time series causalities without bu...
06/14/2020 ∙ by Zhiheng Zhang, et al. ∙ 0 ∙ share

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Brain-inspired global-local hybrid learning towards human-like intelligence

The combination of neuroscience-oriented and computer-science-oriented a...
06/05/2020 ∙ by Yujie Wu, et al. ∙ 0 ∙ share

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Nonparametric Score Estimators

Estimating the score, i.e., the gradient of log density function, from a...
05/20/2020 ∙ by Yuhao Zhou, et al. ∙ 0 ∙ share

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SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models

Standard variational lower bounds used to train latent variable models p...
04/01/2020 ∙ by Yucen Luo, et al. ∙ 8 ∙ share

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Towards Privacy Protection by Generating Adversarial Identity Masks

As billions of personal data such as photos are shared through social me...
03/15/2020 ∙ by Xiao Yang, et al. ∙ 5 ∙ share

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Triple Memory Networks: a Brain-Inspired Method for Continual Learning

Continual acquisition of novel experience without interfering previously...
03/06/2020 ∙ by Liyuan Wang, et al. ∙ 0 ∙ share

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VFlow: More Expressive Generative Flows with Variational Data Augmentation

Generative flows are promising tractable models for density modeling tha...
02/22/2020 ∙ by Jianfei Chen, et al. ∙ 0 ∙ share

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Boosting Adversarial Training with Hypersphere Embedding

Adversarial training (AT) is one of the most effective defenses to impro...
02/20/2020 ∙ by Tianyu Pang, et al. ∙ 13 ∙ share

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A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models

Score matching provides an effective approach to learning flexible unnor...
02/18/2020 ∙ by Ziyu Wang, et al. ∙ 9 ∙ share

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Adversarial Distributional Training for Robust Deep Learning

Adversarial training (AT) is among the most effective techniques to impr...
02/14/2020 ∙ by Zhijie Deng, et al. ∙ 0 ∙ share

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Benchmarking Adversarial Robustness

Deep neural networks are vulnerable to adversarial examples, which becom...
12/26/2019 ∙ by Yinpeng Dong, et al. ∙ 0 ∙ share

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Triple Generative Adversarial Networks

Generative adversarial networks (GANs) have shown promise in image gener...
12/20/2019 ∙ by Chongxuan Li, et al. ∙ 28 ∙ share

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Control variates and Rao-Blackwellization for deterministic sweep Markov chains

We study control variate methods for Markov chain Monte Carlo (MCMC) in ...
12/14/2019 ∙ by Stephen Berg, et al. ∙ 0 ∙ share

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The Search for Sparse, Robust Neural Networks

Recent work on deep neural network pruning has shown there exist sparse ...
12/05/2019 ∙ by Justin Cosentino, et al. ∙ 0 ∙ share

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Design and Interpretation of Universal Adversarial Patches in Face Detection

We consider universal adversarial patches for faces - small visual eleme...
11/30/2019 ∙ by Xiao Yang, et al. ∙ 0 ∙ share

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DBSN: Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structures

Bayesian neural networks (BNNs) introduce uncertainty estimation to deep...
11/22/2019 ∙ by Zhijie Deng, et al. ∙ 13 ∙ share

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Generative Well-intentioned Networks

We propose Generative Well-intentioned Networks (GWINs), a novel framewo...
10/28/2019 ∙ by Justin Cosentino, et al. ∙ 0 ∙ share

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Understanding and Stabilizing GANs' Training Dynamics with Control Theory

Generative adversarial networks (GANs) have made significant progress on...
09/29/2019 ∙ by Kun Xu, et al. ∙ 0 ∙ share

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Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks

It has been widely recognized that adversarial examples can be easily cr...
09/25/2019 ∙ by Tianyu Pang, et al. ∙ 0 ∙ share

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A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels

Recently deep neural networks have shown their capacity to memorize trai...
09/20/2019 ∙ by Yucen Luo, et al. ∙ 0 ∙ share

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Cross-Lingual Contextual Word Embeddings Mapping With Multi-Sense Words In Mind

Recent work in cross-lingual contextual word embedding learning cannot h...
09/18/2019 ∙ by Zheng Zhang, et al. ∙ 0 ∙ share

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Spatial Process Decomposition for Quantitative Imaging Biomarkers Using Multiple Images of Varying Shapes

Quantitative imaging biomarkers (QIB) are extracted from medical images ...
07/26/2019 ∙ by ShengLi Tzeng, et al. ∙ 0 ∙ share

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Improving Black-box Adversarial Attacks with a Transfer-based Prior

We consider the black-box adversarial setting, where the adversary has t...
06/17/2019 ∙ by Shuyu Cheng, et al. ∙ 0 ∙ share

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Multi-objects Generation with Amortized Structural Regularization

Deep generative models (DGMs) have shown promise in image generation. Ho...
06/10/2019 ∙ by Kun Xu, et al. ∙ 0 ∙ share

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Automatic Realistic Music Video Generation from Segments of Youtube Videos

A Music Video (MV) is a video aiming at visually illustrating or extendi...
05/29/2019 ∙ by Sarah Gross, et al. ∙ 0 ∙ share

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Scalable Training of Inference Networks for Gaussian-Process Models

Inference in Gaussian process (GP) models is computationally challenging...
05/27/2019 ∙ by Jiaxin Shi, et al. ∙ 0 ∙ share

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Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness

Previous work shows that adversarially robust generalization requires la...
05/25/2019 ∙ by Tianyu Pang, et al. ∙ 0 ∙ share

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Boosting Generative Models by Leveraging Cascaded Meta-Models

Deep generative models are effective methods of modeling data. However, ...
05/11/2019 ∙ by Fan Bao, et al. ∙ 0 ∙ share

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Efficient Decision-based Black-box Adversarial Attacks on Face Recognition

Face recognition has obtained remarkable progress in recent years due to...
04/09/2019 ∙ by Yinpeng Dong, et al. ∙ 0 ∙ share

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Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks

Deep neural networks are vulnerable to adversarial examples, which can m...
04/05/2019 ∙ by Yinpeng Dong, et al. ∙ 0 ∙ share

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Cluster Alignment with a Teacher for Unsupervised Domain Adaptation

Deep learning methods have shown promise in unsupervised domain adaptati...
03/24/2019 ∙ by Zhijie Deng, et al. ∙ 0 ∙ share

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Function Space Particle Optimization for Bayesian Neural Networks

While Bayesian neural networks (BNNs) have drawn increasing attention, t...
02/26/2019 ∙ by Ziyu Wang, et al. ∙ 24 ∙ share

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Batch Virtual Adversarial Training for Graph Convolutional Networks

We present batch virtual adversarial training (BVAT), a novel regulariza...
02/25/2019 ∙ by Zhijie Deng, et al. ∙ 0 ∙ share

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Understanding MCMC Dynamics as Flows on the Wasserstein Space

It is known that the Langevin dynamics used in MCMC is the gradient flow...
02/01/2019 ∙ by Chang Liu, et al. ∙ 0 ∙ share

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Secure Massive MIMO Communication with Low-resolution DACs

In this paper, we investigate secure transmission in a massive multiple-...
01/28/2019 ∙ by Jindan Xu, et al. ∙ 0 ∙ share

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Reward Shaping via Meta-Learning

Reward shaping is one of the most effective methods to tackle the crucia...
01/27/2019 ∙ by Haosheng Zou, et al. ∙ 0 ∙ share

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Improving Adversarial Robustness via Promoting Ensemble Diversity

Though deep neural networks have achieved significant progress on variou...
01/25/2019 ∙ by Tianyu Pang, et al. ∙ 0 ∙ share

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Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples

Sometimes it is not enough for a DNN to produce an outcome. For example,...
01/25/2019 ∙ by Yinpeng Dong, et al. ∙ 0 ∙ share

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Adversarial Variational Inference and Learning in Markov Random Fields

Markov random fields (MRFs) find applications in a variety of machine le...
01/24/2019 ∙ by Chongxuan Li, et al. ∙ 18 ∙ share

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Transferable Adversarial Attacks for Image and Video Object Detection

Adversarial examples have been demonstrated to threaten many computer vi...
11/30/2018 ∙ by Xingxing Wei, et al. ∙ 8 ∙ share

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MIMO Channel Information Feedback Using Deep Recurrent Network

In a multiple-input multiple-output (MIMO) system, the availability of c...
11/19/2018 ∙ by Chao Lu, et al. ∙ 0 ∙ share

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Composite Binary Decomposition Networks

Binary neural networks have great resource and computing efficiency, whi...
11/16/2018 ∙ by You Qiaoben, et al. ∙ 0 ∙ share

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Semi-crowdsourced Clustering with Deep Generative Models

We consider the semi-supervised clustering problem where crowdsourcing p...
10/29/2018 ∙ by Yucen Luo, et al. ∙ 0 ∙ share

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Jun Zhu

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