1 code implementation • 16 Jan 2024 • Zhepeng Cen, Zuxin Liu, Zitong Wang, Yihang Yao, Henry Lam, Ding Zhao
Offline reinforcement learning (RL) offers a promising direction for learning policies from pre-collected datasets without requiring further interactions with the environment.
no code implementations • 17 Oct 2023 • Henry Lam, Zitong Wang
Stochastic gradient descent (SGD) or stochastic approximation has been widely used in model training and stochastic optimization.
1 code implementation • 3 Aug 2023 • Fu Lin, Xuexiong Luo, Jia Wu, Jian Yang, Shan Xue, Zitong Wang, Haonan Gong
Then, two competing student models trained by normal and abnormal graphs respectively fit graph representations of the teacher model in terms of node-level and graph-level representation perspectives.
no code implementations • 31 Jul 2023 • Yushan Li, Zitong Wang, Jianping He, Cailian Chen, Xinping Guan
More importantly, we amend the noise design by introducing one-lag time dependence, achieving the zero state deviation and the non-zero topology inference error in the asymptotic sense simultaneously.
1 code implementation • 22 Jul 2023 • Fu Lin, Haonan Gong, Mingkang Li, Zitong Wang, Yue Zhang, Xuexiong Luo
The previous works have observed that abnormal graphs mainly show node-level and graph-level anomalies, but these methods equally treat two anomaly forms above in the evaluation of abnormal graphs, which is contrary to the fact that different types of abnormal graph data have different degrees in terms of node-level and graph-level anomalies.
no code implementations • 28 Jun 2023 • Guoqiang Yang, XiaoWen Chang, Zitong Wang, Min Yang
The phenomenon of seat occupancy in university libraries is a prevalent issue.
no code implementations • 4 Dec 2019 • Zitong Wang, Li Wang, Raymond Chan, Tieyong Zeng
A novel approach is then proposed to construct the graph of the input data from the learned graph of a small number of vertexes with some preferred properties.
1 code implementation • 26 Sep 2019 • Guilin Li, Xing Zhang, Zitong Wang, Matthias Tan, Jiashi Feng, Zhenguo Li, Tong Zhang
Recently, the efficiency of automatic neural architecture design has been significantly improved by gradient-based search methods such as DARTS.