13 citations · 15 across the 3 of their papers we have counts for
5 papers
Addressing Action Oscillations through Learning Policy Inertia
Chen Chen, Hongyao Tang, Jianye Hao +2
Deep reinforcement learning (DRL) algorithms have been demonstrated to be effective in a wide range of challenging decision making and control tasks. However, these methods typical…
Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction
Hongyao Tang, Jianye Hao, Guangyong Chen +6
Value function is the central notion of Reinforcement Learning (RL). Value estimation, especially with function approximation, can be challenging since it involves the stochasticit…
Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning
Haotian Fu, Hongyao Tang, Jianye Hao +4
Context, the embedding of previous collected trajectories, is a powerful construct for Meta-Reinforcement Learning (Meta-RL) algorithms. By conditioning on an effective context, Me…
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Zhaohan Xiong, Qing Xia, Zhiqiang Hu +41
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial fir…
TARANET: Traffic-Analysis Resistant Anonymity at the NETwork layer
Chen Chen, Daniele E. Asoni, Adrian Perrig +3
Modern low-latency anonymity systems, no matter whether constructed as an overlay or implemented at the network layer, offer limited security guarantees against traffic analysis. O…