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Found 7 papers, 0 papers with code
Theoretical Hardness and Tractability of POMDPs in RL with Partial Online State Information
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.
Self-training with dual uncertainty for semi-supervised medical image segmentation
In the field of semi-supervised medical image segmentation, the shortage of labeled data is the fundamental problem.
HD-GCN:A Hybrid Diffusion Graph Convolutional Network
HD-GCN utilizes hybrid diffusion by combining information diffusion between neighborhood nodes in the feature space and adjacent nodes in the adjacency matrix.
Near-Optimal Adversarial Reinforcement Learning with Switching Costs
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.
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints
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.
Transfer Learning Application of Self-supervised Learning in ARPES
Recent development in angle-resolved photoemission spectroscopy (ARPES) technique involves spatially resolving samples while maintaining the high-resolution feature of momentum space.
Invertible Mask Network for Face Privacy-Preserving
Then, put the "Mask" face onto the protected face and generate the masked face, in which the masked face is indistinguishable from "Mask" face.
