no code implementations • 14 Jun 2023 • Ming Shi, Yingbin Liang, Ness Shroff
However, existing theoretical results have shown that learning in POMDPs is intractable in the worst case, where the main challenge lies in the lack of latent state information.
no code implementations • 10 Apr 2023 • Zhanhong Qiu, Haitao Gan, Ming Shi, Zhongwei Huang, Zhi Yang
In the field of semi-supervised medical image segmentation, the shortage of labeled data is the fundamental problem.
no code implementations • 31 Mar 2023 • Zhi Yang, Kang Li, Haitao Gan, Zhongwei Huang, Ming Shi
HD-GCN utilizes hybrid diffusion by combining information diffusion between neighborhood nodes in the feature space and adjacent nodes in the adjacency matrix.
no code implementations • 8 Feb 2023 • Ming Shi, Yingbin Liang, Ness Shroff
Our lower bound indicates that, due to the fundamental challenge of switching costs in adversarial RL, the best achieved regret (whose dependency on $T$ is $\tilde{O}(\sqrt{T})$) in static RL with switching costs (as well as adversarial RL without switching costs) is no longer achievable.
no code implementations • 8 Feb 2023 • Ming Shi, Yingbin Liang, Ness Shroff
In many applications of Reinforcement Learning (RL), it is critically important that the algorithm performs safely, such that instantaneous hard constraints are satisfied at each step, and unsafe states and actions are avoided.
no code implementations • 23 Aug 2022 • Sandy Adhitia Ekahana, Genta Indra Winata, Y. Soh, Gabriel Aeppli, Radovic Milan, Ming Shi
Recent development in angle-resolved photoemission spectroscopy (ARPES) technique involves spatially resolving samples while maintaining the high-resolution feature of momentum space.
no code implementations • 19 Apr 2022 • Yang Yang, Yiyang Huang, Ming Shi, Kejiang Chen, Weiming Zhang, Nenghai Yu
Then, put the "Mask" face onto the protected face and generate the masked face, in which the masked face is indistinguishable from "Mask" face.