no code implementations • 9 Feb 2023 • Ziang Yan, Shusen Wang, Guorui Zhou, Jingjian Lin, Peng Jiang
Recent advances in this field often address the budget allocation problem using a two-stage paradigm: the first stage estimates the individual-level treatment effects using causal inference algorithms, and the second stage invokes integer programming techniques to find the optimal budget allocation solution.
1 code implementation • 6 Feb 2021 • Haipeng Zhang, Zhong Cao, Ziang Yan, ChangShui Zhang
For visual object recognition tasks, the illumination variations can cause distinct changes in object appearance and thus confuse the deep neural network based recognition models.
Ranked #1 on Traffic Sign Recognition on TopLogo-10
no code implementations • ICLR 2021 • Ziang Yan, Yiwen Guo, Jian Liang, ChangShui Zhang
To craft black-box adversarial examples, adversaries need to query the victim model and take proper advantage of its feedback.
1 code implementation • 26 Oct 2020 • Yuhai Song, Zhong Cao, Kailun Wu, Ziang Yan, ChangShui Zhang
The idea of unfolding iterative algorithms as deep neural networks has been widely applied in solving sparse coding problems, providing both solid theoretical analysis in convergence rate and superior empirical performance.
no code implementations • 7 Jun 2020 • Yiwen Sun, Yulu Wang, Kun fu, Zheng Wang, Ziang Yan, Chang-Shui Zhang, Jieping Ye
Estimated time of arrival (ETA) is one of the most important services in intelligent transportation systems and becomes a challenging spatial-temporal (ST) data mining task in recent years.
1 code implementation • 14 Nov 2019 • Ziang Yan, Yiwen Guo, Chang-Shui Zhang
The tremendous recent success of deep neural networks (DNNs) has sparked a surge of interest in understanding their predictive ability.
2 code implementations • NeurIPS 2019 • Ziang Yan, Yiwen Guo, Chang-Shui Zhang
Unlike the white-box counterparts that are widely studied and readily accessible, adversarial examples in black-box settings are generally more Herculean on account of the difficulty of estimating gradients.
1 code implementation • NeurIPS 2018 • Ziang Yan, Yiwen Guo, Chang-Shui Zhang
Despite the efficacy on a variety of computer vision tasks, deep neural networks (DNNs) are vulnerable to adversarial attacks, limiting their applications in security-critical systems.
no code implementations • 19 Mar 2017 • Jiang Lu, Jin Li, Ziang Yan, Chang-Shui Zhang
Given the dataset of seen classes and side information of unseen classes (e. g. attributes), we synthesize feature-level pseudo representations for novel concepts, which allows us access to the formulation of unseen class predictor.
no code implementations • 28 Feb 2017 • Ziang Yan, Jian Liang, Weishen Pan, Jin Li, Chang-Shui Zhang
Object detection when provided image-level labels instead of instance-level labels (i. e., bounding boxes) during training is an important problem in computer vision, since large scale image datasets with instance-level labels are extremely costly to obtain.
no code implementations • 1 Sep 2016 • Junqi Jin, Ziang Yan, Kun fu, Nan Jiang, Chang-Shui Zhang
Deep learning models' architectures, including depth and width, are key factors influencing models' performance, such as test accuracy and computation time.
no code implementations • 29 Aug 2016 • Junqi Jin, Ziang Yan, Kun fu, Nan Jiang, Chang-Shui Zhang
A greedy algorithm with bounds is suggested to solve the transformed problem.