b'Shu-Tao Xia'

Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits

To explore the vulnerability of deep neural networks (DNNs), many attack...
02/21/2021 ∙ by Jiawang Bai, et al. ∙ 0 ∙ share

read it
JSRT: James-Stein Regression Tree

Regression tree (RT) has been widely used in machine learning and data m...
10/18/2020 ∙ by Xingchun Xiang, et al. ∙ 0 ∙ share

read it
DPAttack: Diffused Patch Attacks against Universal Object Detection

Recently, deep neural networks (DNNs) have been widely and successfully ...
10/16/2020 ∙ by Shudeng Wu, et al. ∙ 0 ∙ share

read it
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

read it
Rectified Decision Trees: Exploring the Landscape of Interpretable and Effective Machine Learning

Interpretability and effectiveness are two essential and indispensable r...
08/21/2020 ∙ by Yiming Li, et al. ∙ 33 ∙ share

read it
Backdoor Learning: A Survey

Deep neural networks (DNNs) have demonstrated their power on many widely...
07/17/2020 ∙ by Yiming Li, et al. ∙ 47 ∙ share

read it
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

read it
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

read it
Construction of MDS Euclidean Self-Dual Codes via Two Subsets

The parameters of a q-ary MDS Euclidean self-dual codes are completely d...
05/31/2020 ∙ by Weijun Fang, et al. ∙ 0 ∙ share

read it
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

read it
Matrix Smoothing: A Regularization for DNN with Transition Matrix under Noisy Labels

Training deep neural networks (DNNs) in the presence of noisy labels is ...
03/26/2020 ∙ by Xianbin Lv, et al. ∙ 0 ∙ share

read it
Practical and Bilateral Privacy-preserving Federated Learning

Federated learning, as an emerging distributed training model of neural ...
02/23/2020 ∙ by Yan Feng, et al. ∙ 0 ∙ share

read it
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

read it
Deep Flow Collaborative Network for Online Visual Tracking

The deep learning-based visual tracking algorithms such as MDNet achieve...
11/05/2019 ∙ by Peidong Liu, et al. ∙ 7 ∙ share

read it
Visual Privacy Protection via Mapping Distortion

Data privacy protection is an important research area, which is especial...
11/05/2019 ∙ by Peidong Liu, et al. ∙ 0 ∙ share

read it
Adversarial Defense Via Local Flatness Regularization

Adversarial defense is a popular and important research area. Due to its...
10/27/2019 ∙ by Jia Xu, et al. ∙ 0 ∙ share

read it
Adaptive Regularization of Labels

Recently, a variety of regularization techniques have been widely applie...
08/15/2019 ∙ by Qianggang Ding, et al. ∙ 0 ∙ share

read it
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

read it
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

read it
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

read it
Bayesian linear regression with Student-t assumptions

As an automatic method of determining model complexity using the trainin...
04/15/2016 ∙ by Chaobing Song, et al. ∙ 0 ∙ share

read it
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

read it

Shu-Tao Xia

Featured Co-authors

Jun Zhu

Bo Zhang

Le Song

Hongyuan Zha

Bo Dai

Hang Su

Hao Sun

Yong Jiang

Yong Zhang

Baoyuan Wu

James Bailey

Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits

JSRT: James-Stein Regression Tree

DPAttack: Diffused Patch Attacks against Universal Object Detection

Improving Query Efficiency of Black-box Adversarial Attack

Rectified Decision Trees: Exploring the Landscape of Interpretable and Effective Machine Learning

Backdoor Learning: A Survey

Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters

Temporal Calibrated Regularization for Robust Noisy Label Learning

Construction of MDS Euclidean Self-Dual Codes via Two Subsets

Revisiting Loss Landscape for Adversarial Robustness

Matrix Smoothing: A Regularization for DNN with Transition Matrix under Noisy Labels

Practical and Bilateral Privacy-preserving Federated Learning

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

Deep Flow Collaborative Network for Online Visual Tracking

Visual Privacy Protection via Mapping Distortion

Adversarial Defense Via Local Flatness Regularization

Adaptive Regularization of Labels

Learning Deep Hidden Nonlinear Dynamics from Aggregate Data

Dimensionality-Driven Learning with Noisy Labels

Iterative Learning with Open-set Noisy Labels

Bayesian linear regression with Student-t assumptions

Unifying Decision Trees Split Criteria Using Tsallis Entropy