
Deep Active Learning by Model Interpretability
Recent successes of Deep Neural Networks (DNNs) in a variety of research...
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Training Interpretable Convolutional Neural Networks by Differentiating Classspecific Filters
Convolutional neural networks (CNNs) have been successfully used in a ra...
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A quasiconservative discontinuous Galerkin method for multicomponent flows using the nonoscillatory kinetic flux
In this paper, a high order quasiconservative discontinuous Galerkin (D...
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Delving into the Adversarial Robustness on Face Recognition
Face recognition has recently made substantial progress and achieved hig...
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Efficient Inference of Nonparametric Interaction in Spikingneuron Networks
Hawkes process provides an effective statistical framework for analyzing...
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Absolutely convergent fixedpoint fast sweeping WENO methods for steady state of hyperbolic conservation laws
Fixedpoint iterative sweeping methods were developed in the literature ...
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Calibrated Reliable Regression using Maximum Mean Discrepancy
Accurate quantification of uncertainty is crucial for realworld applica...
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Dynamic Windowlevel Granger Causality of Multichannel Time Series
Granger causality method analyzes the time series causalities without bu...
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Braininspired globallocal hybrid learning towards humanlike intelligence
The combination of neuroscienceoriented and computerscienceoriented a...
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Nonparametric Score Estimators
Estimating the score, i.e., the gradient of log density function, from a...
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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...
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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...
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Triple Memory Networks: a BrainInspired Method for Continual Learning
Continual acquisition of novel experience without interfering previously...
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VFlow: More Expressive Generative Flows with Variational Data Augmentation
Generative flows are promising tractable models for density modeling tha...
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Boosting Adversarial Training with Hypersphere Embedding
Adversarial training (AT) is one of the most effective defenses to impro...
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A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models
Score matching provides an effective approach to learning flexible unnor...
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Adversarial Distributional Training for Robust Deep Learning
Adversarial training (AT) is among the most effective techniques to impr...
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Benchmarking Adversarial Robustness
Deep neural networks are vulnerable to adversarial examples, which becom...
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Triple Generative Adversarial Networks
Generative adversarial networks (GANs) have shown promise in image gener...
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Control variates and RaoBlackwellization for deterministic sweep Markov chains
We study control variate methods for Markov chain Monte Carlo (MCMC) in ...
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The Search for Sparse, Robust Neural Networks
Recent work on deep neural network pruning has shown there exist sparse ...
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Design and Interpretation of Universal Adversarial Patches in Face Detection
We consider universal adversarial patches for faces  small visual eleme...
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DBSN: Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structures
Bayesian neural networks (BNNs) introduce uncertainty estimation to deep...
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Generative Wellintentioned Networks
We propose Generative Wellintentioned Networks (GWINs), a novel framewo...
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Understanding and Stabilizing GANs' Training Dynamics with Control Theory
Generative adversarial networks (GANs) have made significant progress on...
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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...
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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...
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CrossLingual Contextual Word Embeddings Mapping With MultiSense Words In Mind
Recent work in crosslingual contextual word embedding learning cannot h...
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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 ...
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Improving Blackbox Adversarial Attacks with a Transferbased Prior
We consider the blackbox adversarial setting, where the adversary has t...
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Multiobjects Generation with Amortized Structural Regularization
Deep generative models (DGMs) have shown promise in image generation. Ho...
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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...
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Scalable Training of Inference Networks for GaussianProcess Models
Inference in Gaussian process (GP) models is computationally challenging...
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Rethinking Softmax CrossEntropy Loss for Adversarial Robustness
Previous work shows that adversarially robust generalization requires la...
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Boosting Generative Models by Leveraging Cascaded MetaModels
Deep generative models are effective methods of modeling data. However, ...
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Efficient Decisionbased Blackbox Adversarial Attacks on Face Recognition
Face recognition has obtained remarkable progress in recent years due to...
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Evading Defenses to Transferable Adversarial Examples by TranslationInvariant Attacks
Deep neural networks are vulnerable to adversarial examples, which can m...
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Cluster Alignment with a Teacher for Unsupervised Domain Adaptation
Deep learning methods have shown promise in unsupervised domain adaptati...
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Function Space Particle Optimization for Bayesian Neural Networks
While Bayesian neural networks (BNNs) have drawn increasing attention, t...
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Batch Virtual Adversarial Training for Graph Convolutional Networks
We present batch virtual adversarial training (BVAT), a novel regulariza...
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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...
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Secure Massive MIMO Communication with Lowresolution DACs
In this paper, we investigate secure transmission in a massive multiple...
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Reward Shaping via MetaLearning
Reward shaping is one of the most effective methods to tackle the crucia...
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Improving Adversarial Robustness via Promoting Ensemble Diversity
Though deep neural networks have achieved significant progress on variou...
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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,...
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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...
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Transferable Adversarial Attacks for Image and Video Object Detection
Adversarial examples have been demonstrated to threaten many computer vi...
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MIMO Channel Information Feedback Using Deep Recurrent Network
In a multipleinput multipleoutput (MIMO) system, the availability of c...
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Composite Binary Decomposition Networks
Binary neural networks have great resource and computing efficiency, whi...
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Semicrowdsourced Clustering with Deep Generative Models
We consider the semisupervised clustering problem where crowdsourcing p...
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Jun Zhu
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Associate Professor, Computer Science Department at Tsinghua University. Adjunct Faculty, Machine Learning Department at Carnegie Mellon University.