190 citations · 357 across the 10 of their papers we have counts for
10 papers
Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition
Xiao Yang, Chang Liu, Longlong Xu +5
Face recognition is a prevailing authentication solution in numerous biometric applications. Physical adversarial attacks, as an important surrogate, can identify the weaknesses of…
Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk
Chengyang Ying, Xinning Zhou, Hang Su +3
Though deep reinforcement learning (DRL) has obtained substantial success, it may encounter catastrophic failures due to the intrinsic uncertainty of both transition and observatio…
Improving Adversarial Robustness via Promoting Ensemble Diversity
Tianyu Pang, Kun Xu, Chao Du +2
Though deep neural networks have achieved significant progress on various tasks, often enhanced by model ensemble, existing high-performance models can be vulnerable to adversarial…
Message Passing Stein Variational Gradient Descent
Jingwei Zhuo, Chang Liu, Jiaxin Shi +3
Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation…
Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation
Bei Chen, Ning Chen, Jun Zhu +2
We present a discriminative nonparametric latent feature relational model (LFRM) for link prediction to automatically infer the dimensionality of latent features. Under the generic…
Dropout Training for SVMs with Data Augmentation
Ning Chen, Jun Zhu, Jianfei Chen +1
Dropout and other feature noising schemes have shown promising results in controlling over-fitting by artificially corrupting the training data. Though extensive theoretical and em…
Dropout Training for Support Vector Machines
Ning Chen, Jun Zhu, Jianfei Chen +1
Dropout and other feature noising schemes have shown promising results in controlling over-fitting by artificially corrupting the training data. Though extensive theoretical and em…
Gibbs Max-margin Topic Models with Data Augmentation
Jun Zhu, Ning Chen, Hugh Perkins +1
Max-margin learning is a powerful approach to building classifiers and structured output predictors. Recent work on max-margin supervised topic models has successfully integrated i…
Discriminative Relational Topic Models
Ning Chen, Jun Zhu, Fei Xia +1
Many scientific and engineering fields involve analyzing network data. For document networks, relational topic models (RTMs) provide a probabilistic generative process to describe…
Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs
Jun Zhu, Ning Chen, Eric P. Xing
Existing Bayesian models, especially nonparametric Bayesian methods, rely on specially conceived priors to incorporate domain knowledge for discovering improved latent representati…