Yisen Wang

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Tsinghua University

Dissecting the Diffusion Process in Linear Graph Convolutional Networks

Graph Convolutional Networks (GCNs) have attracted more and more attenti...
02/22/2021 ∙ by Yifei Wang, et al. ∙ 0 ∙ share

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What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space

Deep neural networks (DNNs) have been widely adopted in different applic...
01/18/2021 ∙ by Shihao Zhao, et al. ∙ 16 ∙ share

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Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions

Understanding the actions of both humans and artificial intelligence (AI...
01/17/2021 ∙ by Nodens Koren, et al. ∙ 0 ∙ share

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Unlearnable Examples: Making Personal Data Unexploitable

The volume of "free" data on the internet has been key to the current su...
01/13/2021 ∙ by Hanxun Huang, et al. ∙ 0 ∙ share

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A Unified Approach to Interpreting and Boosting Adversarial Transferability

In this paper, we use the interaction inside adversarial perturbations t...
10/08/2020 ∙ by Xin Wang, et al. ∙ 0 ∙ share

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Improving Query Efficiency of Black-box Adversarial Attack

Deep neural networks (DNNs) have demonstrated excellent performance on v...
09/24/2020 ∙ by Yang Bai, et al. ∙ 11 ∙ share

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Temporal Calibrated Regularization for Robust Noisy Label Learning

Deep neural networks (DNNs) exhibit great success on many tasks with the...
07/01/2020 ∙ by Dongxian Wu, et al. ∙ 0 ∙ share

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Normalized Loss Functions for Deep Learning with Noisy Labels

Robust loss functions are essential for training accurate deep neural ne...
06/24/2020 ∙ by Xingjun Ma, et al. ∙ 49 ∙ share

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Revisiting Loss Landscape for Adversarial Robustness

The study on improving the robustness of deep neural networks against ad...
04/13/2020 ∙ by Dongxian Wu, et al. ∙ 0 ∙ share

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Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets

Skip connections are an essential component of current state-of-the-art ...
02/14/2020 ∙ by Dongxian Wu, et al. ∙ 5 ∙ share

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Symmetric Cross Entropy for Robust Learning with Noisy Labels

Training accurate deep neural networks (DNNs) in the presence of noisy l...
08/16/2019 ∙ by Yisen Wang, et al. ∙ 0 ∙ share

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Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems

Deep neural networks (DNNs) have become popular for medical image analys...
07/24/2019 ∙ by Xingjun Ma, et al. ∙ 6 ∙ share

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Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to transfer the classifier learned f...
06/10/2019 ∙ by Dong-Dong Chen, et al. ∙ 5 ∙ share

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Learning Deep Hidden Nonlinear Dynamics from Aggregate Data

Learning nonlinear dynamics from diffusion data is a challenging problem...
07/22/2018 ∙ by Yisen Wang, et al. ∙ 0 ∙ share

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Dimensionality-Driven Learning with Noisy Labels

Datasets with significant proportions of noisy (incorrect) class labels ...
06/07/2018 ∙ by Xingjun Ma, et al. ∙ 0 ∙ share

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Decoupled Networks

Inner product-based convolution has been a central component of convolut...
04/22/2018 ∙ by Weiyang Liu, et al. ∙ 0 ∙ share

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Iterative Learning with Open-set Noisy Labels

Large-scale datasets possessing clean label annotations are crucial for ...
03/31/2018 ∙ by Yisen Wang, et al. ∙ 0 ∙ share

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Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

Deep Neural Networks (DNNs) have recently been shown to be vulnerable ag...
01/08/2018 ∙ by Xingjun Ma, et al. ∙ 0 ∙ share

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Residual Convolutional CTC Networks for Automatic Speech Recognition

Deep learning approaches have been widely used in Automatic Speech Recog...
02/24/2017 ∙ by Yisen Wang, et al. ∙ 0 ∙ share

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Unifying Decision Trees Split Criteria Using Tsallis Entropy

The construction of efficient and effective decision trees remains a key...
11/25/2015 ∙ by Yisen Wang, et al. ∙ 0 ∙ share

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Yisen Wang

Featured Co-authors

Bo Li

Xin Wang

Le Song

Dawn Song

Hongyuan Zha

Zhouchen Lin

Bo Dai

Zhi-Hua Zhou

Yu-Gang Jiang

Yuan Luo

Jinfeng Yi

Dissecting the Diffusion Process in Linear Graph Convolutional Networks

What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space

Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions

Unlearnable Examples: Making Personal Data Unexploitable

A Unified Approach to Interpreting and Boosting Adversarial Transferability

Improving Query Efficiency of Black-box Adversarial Attack

Temporal Calibrated Regularization for Robust Noisy Label Learning

Normalized Loss Functions for Deep Learning with Noisy Labels

Revisiting Loss Landscape for Adversarial Robustness

Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets

Symmetric Cross Entropy for Robust Learning with Noisy Labels

Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems

Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation

Learning Deep Hidden Nonlinear Dynamics from Aggregate Data

Dimensionality-Driven Learning with Noisy Labels

Decoupled Networks

Iterative Learning with Open-set Noisy Labels

Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

Residual Convolutional CTC Networks for Automatic Speech Recognition

Unifying Decision Trees Split Criteria Using Tsallis Entropy