58 citations · 305 across the 33 of their papers we have counts for
9 papers · 1 filter
Open-sourced Dataset Protection via Backdoor Watermarking
Yiming Li, Ziqi Zhang, Jiawang Bai +3
The rapid development of deep learning has benefited from the release of some high-quality open-sourced datasets (, ImageNet), which allows researchers to easily verify the e…
Backdoor Attack against Speaker Verification
Tongqing Zhai, Yiming Li, Ziqi Zhang +3
Speaker verification has been widely and successfully adopted in many mission-critical areas for user identification. The training of speaker verification requires a large amount o…
Improving Query Efficiency of Black-box Adversarial Attack
Yang Bai, Yuyuan Zeng, Yong Jiang +3
Deep neural networks (DNNs) have demonstrated excellent performance on various tasks, however they are under the risk of adversarial examples that can be easily generated when the…
Rectified Decision Trees: Exploring the Landscape of Interpretable and Effective Machine Learning
Yiming Li, Jiawang Bai, Jiawei Li +3
Interpretability and effectiveness are two essential and indispensable requirements for adopting machine learning methods in reality. In this paper, we propose a knowledge distilla…
Neural Network-based Automatic Factor Construction
Jie Fang, Jianwu Lin, Shutao Xia +3
Instead of conducting manual factor construction based on traditional and behavioural finance analysis, academic researchers and quantitative investment managers have leveraged Gen…
Backdoor Learning: A Survey
Yiming Li, Yong Jiang, Zhifeng Li +1
Backdoor attack intends to embed hidden backdoor into deep neural networks (DNNs), so that the attacked models perform well on benign samples, whereas their predictions will be mal…
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization
Chaobing Song, Yong Jiang, Yi Ma
In this paper, we introduce a simplified and unified method for finite-sum convex optimization, named \emph{Variance Reduction via Accelerated Dual Averaging (VRADA)}. In both gene…
Breaking the Optimal Rate for a Class of Minimax Problems
Chaobing Song, Yong Jiang, Yi Ma
It is known that for convex optimization , the best possible rate of first order accelerated methods is . However, for th…
Toward Adversarial Robustness via Semi-supervised Robust Training
Yiming Li, Baoyuan Wu, Yan Feng +4
Adversarial examples have been shown to be the severe threat to deep neural networks (DNNs). One of the most effective adversarial defense methods is adversarial training (AT) thro…