no code implementations • 30 Apr 2024 • Wang Zhang, TingTing Li, Yuntian Zhang, Gensheng Pei, Xiruo Jiang, Yazhou Yao
Nevertheless, annotated data is frequently limited in the field of remote sensing image matching.
no code implementations • 25 Mar 2024 • Qin Tian, Wenjun Wang, Chen Zhao, Minglai Shao, Wang Zhang, Dong Li
Traditional machine learning methods heavily rely on the independent and identically distribution assumption, which imposes limitations when the test distribution deviates from the training distribution.
no code implementations • 18 Jan 2024 • Cheng Wang, Chuwen Wang, Wang Zhang, Shirong Zeng, Yu Zhao, Ronghui Ning, Changjun Jiang
As artificial intelligence becomes increasingly prevalent in scientific research, data-driven methodologies appear to overshadow traditional methods in resolving scientific problems.
1 code implementation • 16 Dec 2023 • Wang Zhang, Ziwen Ma, Subhro Das, Tsui-Wei Weng, Alexandre Megretski, Luca Daniel, Lam M. Nguyen
Neural networks are powerful tools in various applications, and quantifying their uncertainty is crucial for reliable decision-making.
1 code implementation • 11 Feb 2023 • Wang Zhang, Tsui-Wei Weng, Subhro Das, Alexandre Megretski, Luca Daniel, Lam M. Nguyen
Deep neural networks (DNN) have shown great capacity of modeling a dynamical system; nevertheless, they usually do not obey physics constraints such as conservation laws.
1 code implementation • 18 Jan 2022 • Xuan Guo, Pengfei Jiao, Ting Pan, Wang Zhang, Mengyu Jia, Danyang Shi, Wenjun Wang
Capturing structural similarity has been a hot topic in the field of network embedding recently due to its great help in understanding the node functions and behaviors.
no code implementations • 29 Sep 2021 • Wang Zhang, Lam M. Nguyen, Subhro Das, Pin-Yu Chen, Sijia Liu, Alexandre Megretski, Luca Daniel, Tsui-Wei Weng
In verification-based robust training, existing methods utilize relaxation based methods to bound the worst case performance of neural networks given certain perturbation.
1 code implementation • 18 Jul 2021 • Pengfei Jiao, Xuan Guo, Ting Pan, Wang Zhang, Yulong Pei
A wide variety of NE methods focus on the proximity of networks.
2 code implementations • NeurIPS 2021 • Tuomas Oikarinen, Wang Zhang, Alexandre Megretski, Luca Daniel, Tsui-Wei Weng
To address this issue, we propose RADIAL-RL, a principled framework to train reinforcement learning agents with improved robustness against $l_p$-norm bounded adversarial attacks.