1 code implementation • 7 Feb 2024 • Chengxing Xie, Canyu Chen, Feiran Jia, Ziyu Ye, Kai Shu, Adel Bibi, Ziniu Hu, Philip Torr, Bernard Ghanem, Guohao Li
In addition, we probe into the biases in agent trust and the differences in agent trust towards agents and humans.
no code implementations • 3 May 2023 • Arman Rahbar, Ziyu Ye, Yuxin Chen, Morteza Haghir Chehreghani
Specifically, we employ a surrogate information acquisition function based on adaptive submodularity to actively query feature values with a minimal cost, while using a posterior sampling scheme to maintain a low regret for online prediction.
no code implementations • 6 Mar 2023 • Junyu Liu, Minzhao Liu, Jin-Peng Liu, Ziyu Ye, Yunfei Wang, Yuri Alexeev, Jens Eisert, Liang Jiang
Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process.
1 code implementation • 16 May 2021 • Ziyu Ye, Yuxin Chen, Haitao Zheng
We also provide an extensive empirical study on how a biased training anomaly set affects the anomaly score function and therefore the detection performance on different anomaly classes.
1 code implementation • 1 Jan 2021 • Ziyu Ye, Yuxin Chen, Haitao Zheng
Given two different anomaly score functions, we formally define their difference in performance as the relative scoring bias of the anomaly detectors.
Semi-supervised Anomaly Detection Supervised Anomaly Detection +1
no code implementations • 15 Jul 2019 • Ziyu Ye, Andrew Gilman, Qihang Peng, Kelly Levick, Pamela Cosman, Larry Milstein
Given abundant training data and computational and memory resources, CNN, RNN, and BiRNN are shown to achieve similar performance.
no code implementations • 15 Jul 2019 • Ziyu Ye, Qihang Peng, Kelly Levick, Hui Rong, Andrew Gilman, Pamela Cosman, Larry Milstein
The result displays the neural network's potential in exploiting implicit and incomplete knowledge about the signal's structure.