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Wei Zhang
393 publications
Wei Chen
332 publications
Jun Zhu
239 publications
Bo Zhang
226 publications
Shuicheng Yan
187 publications
Max Welling
167 publications
Jingdong Wang
123 publications
Lu Wang
104 publications
Dongmei Zhang
84 publications
Jian Xu
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Kun Xu
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∙ 08/01/2023

Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction

Learning from positive and unlabeled data is known as positive-unlabeled...

0 Zhangchi Zhu, et al. ∙

∙ 07/04/2023

AdAM: Few-Shot Image Generation via Adaptation-Aware Kernel Modulation

Few-shot image generation (FSIG) aims to learn to generate new and diver...

0 Yunqing Zhao, et al. ∙

∙ 05/31/2023

Efficient Diffusion Policies for Offline Reinforcement Learning

Offline reinforcement learning (RL) aims to learn optimal policies from ...

0 Bingyi Kang, et al. ∙

∙ 05/26/2023

On Evaluating Adversarial Robustness of Large Vision-Language Models

Large vision-language models (VLMs) such as GPT-4 have achieved unpreced...

0 Yunqing Zhao, et al. ∙

∙ 05/03/2023

Nonparametric Generative Modeling with Conditional and Locally-Connected Sliced-Wasserstein Flows

Sliced-Wasserstein Flow (SWF) is a promising approach to nonparametric g...

0 Chao Du, et al. ∙

∙ 04/13/2023

CoSDA: Continual Source-Free Domain Adaptation

Without access to the source data, source-free domain adaptation (SFDA) ...

0 Haozhe Feng, et al. ∙

∙ 03/17/2023

A Recipe for Watermarking Diffusion Models

Recently, diffusion models (DMs) have demonstrated their advantageous po...

0 Yunqing Zhao, et al. ∙

∙ 02/21/2023

On Calibrating Diffusion Probabilistic Models

Recently, diffusion probabilistic models (DPMs) have achieved promising ...

0 Tianyu Pang, et al. ∙

∙ 02/09/2023

Better Diffusion Models Further Improve Adversarial Training

It has been recognized that the data generated by the denoising diffusio...

0 Zekai Wang, et al. ∙

∙ 02/09/2023

Bag of Tricks for Training Data Extraction from Language Models

With the advance of language models, privacy protection is receiving mor...

0 Weichen Yu, et al. ∙

∙ 01/28/2023

Does Federated Learning Really Need Backpropagation?

Federated learning (FL) is a general principle for decentralized clients...

0 Haozhe Feng, et al. ∙

∙ 11/25/2020

Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning

Modern online advertising systems inevitably rely on personalization met...

0 Chao Du, et al. ∙

∙ 05/25/2019

Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness

Previous work shows that adversarially robust generalization requires la...

0 Tianyu Pang, et al. ∙

∙ 01/25/2019

Improving Adversarial Robustness via Promoting Ensemble Diversity

Though deep neural networks have achieved significant progress on variou...

0 Tianyu Pang, et al. ∙

∙ 01/24/2019

Adversarial Variational Inference and Learning in Markov Random Fields

Markov random fields (MRFs) find applications in a variety of machine le...

18 Chongxuan Li, et al. ∙

∙ 07/10/2018

Learning Implicit Generative Models by Teaching Explicit Ones

Implicit generative models are difficult to train as no explicit probabi...

0 Chao Du, et al. ∙

∙ 02/26/2018

Max-Mahalanobis Linear Discriminant Analysis Networks

A deep neural network (DNN) consists of a nonlinear transformation from ...

0 Tianyu Pang, et al. ∙

∙ 12/09/2016

Field-Programmable Crossbar Array (FPCA) for Reconfigurable Computing

For decades, advances in electronics were directly driven by the scaling...

0 Mohammed A. Zidan, et al. ∙

∙ 06/19/2014

Inner Product Similarity Search using Compositional Codes

This paper addresses the nearest neighbor search problem under inner pro...

0 Chao Du, et al. ∙

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