1 code implementation • 2 Feb 2024 • Dingcheng Yang, Yang Bai, Xiaojun Jia, Yang Liu, Xiaochun Cao, Wenjian Yu
The MMP-Attack shows a notable advantage over existing works with superior universality and transferability, which can effectively attack commercial text-to-image (T2I) models such as DALL-E 3.
2 code implementations • 14 Apr 2023 • Dingcheng Yang, Wenjian Yu, Zihao Xiao, Jiaqi Luo
In this paper, we propose to improve the transferability of adversarial examples in the transfer-based attack via masking unimportant parameters (MUP).
2 code implementations • 16 Jun 2022 • Dingcheng Yang, Zihao Xiao, Wenjian Yu
This paper proposes a method for training a surrogate model with dark knowledge to boost the transferability of the adversarial examples generated by the surrogate model.
1 code implementation • 14 Jul 2021 • Dingcheng Yang, Wenjian Yu, Yuanbo Guo, Wenjie Liang
Accurate capacitance extraction is becoming more important for designing integrated circuits under advanced process technology.
1 code implementation • 3 Dec 2020 • Jiabao Zhang, Shenghua Liu, Wenting Hou, Siddharth Bhatia, HuaWei Shen, Wenjian Yu, Xueqi Cheng
Therefore, we propose a fast streaming algorithm, AugSplicing, which can detect the top dense blocks by incrementally splicing the previous detection with the incoming ones in new tuples, avoiding re-runs over all the history data at every tracking time step.
1 code implementation • 4 Sep 2020 • Xiangyun Ding, Wenjian Yu, Yuyang Xie, Shenghua Liu
The proposed model-based CF approach is able to efficiently process the MovieLens data with 20M ratings and exhibits more than 10X speedup over the regularized matrix factorization based approach [2] and the fast singular value thresholding approach [3] with comparable or better accuracy.
2 code implementations • 8 Jul 2020 • Xiao Yang, Dingcheng Yang, Yinpeng Dong, Hang Su, Wenjian Yu, Jun Zhu
Based on large-scale evaluations, the commercial FR API services fail to exhibit acceptable performance on robustness evaluation, and we also draw several important conclusions for understanding the adversarial robustness of FR models and providing insights for the design of robust FR models.
no code implementations • 21 Mar 2020 • Dingcheng Yang, Wenjian Yu, Ao Zhou, Haoyuan Mu, Gary Yao, Xiaoyi Wang
In this work, we propose an effective scheme (called DP-Net) for compressing the deep neural networks (DNNs).
no code implementations • 2 Jan 2020 • Cong Chen, Kim Batselier, Wenjian Yu, Ngai Wong
In this paper, we propose a tensor train (TT)-based kernel technique for the first time, and apply it to the conventional support vector machine (SVM) for image classification.
2 code implementations • 16 Oct 2018 • Xu Feng, Yuyang Xie, Mingye Song, Wenjian Yu, Jie Tang
The algorithm has similar accuracy to the basic randomized SVD (rPCA) algorithm (Halko et al., 2011), but is largely optimized for sparse data.
1 code implementation • 16 Oct 2018 • Xu Feng, Wenjian Yu, Yaohang Li
Then, with the rSVD-BKI algorithm and a new subspace recycling technique, we accelerate the singular value thresholding (SVT) method in [1] to realize faster matrix completion.
no code implementations • 26 Jul 2018 • Yuzhe Ma, Ran Chen, Wei Li, Fanhua Shang, Wenjian Yu, Minsik Cho, Bei Yu
To address this issue, various approximation techniques have been investigated, which seek for a light weighted network with little performance degradation in exchange of smaller model size or faster inference.
1 code implementation • 17 Apr 2018 • Ching-Yun Ko, Kim Batselier, Wenjian Yu, Ngai Wong
We propose a new tensor completion method based on tensor trains.
no code implementations • 25 Apr 2017 • Wenjian Yu, Yu Gu, Jian Li, Shenghua Liu, Yaohang Li
Principal component analysis (PCA) is a fundamental dimension reduction tool in statistics and machine learning.
no code implementations • 14 Apr 2017 • Yaohang Li, Wenjian Yu
In this paper, we present a fast implementation of the Singular Value Thresholding (SVT) algorithm for matrix completion.
Numerical Analysis