2 citations · 2 across the 12 of their papers we have counts for
6 papers · 1 filter
DEFlow: Deep Equilibrium Flow Maximum-Entropy Reinforcement Learning
Ruslan Ageev, Huaze Tang, Runqing Miao +2
Expressive flow policies can represent multiple high-value actions, but sampling them with few integration steps while retaining tractable action densities remains challenging. We…
Function-Space Empirical Bayes Regularisation with Large Vision-Language Model Priors
Pengcheng Hao, Huaze Tang, Ercan Engin Kuruoglu +1
Bayesian deep learning (BDL) provides a principled framework for reliable uncertainty quantification by combining deep neural networks with Bayesian inference. A central challenge…
E^2-LLM: Bridging Neural Signals and Interpretable Affective Analysis
Fei Ma, Han Lin, Yifan Xie +4
Emotion recognition from electroencephalography (EEG) signals remains challenging due to high inter-subject variability, limited labeled data, and the lack of interpretable reasoni…
Learning More with Less: A Dynamic Dual-Level Down-Sampling Framework for Efficient Policy Optimization
Chao Wang, Tao Yang, Hongtao Tian +5
Critic-free methods like GRPO reduce memory demands by estimating advantages from multiple rollouts but tend to converge slowly, as critical learning signals are diluted by an abun…
Bidirectional Soft Actor-Critic: Leveraging Forward and Reverse KL Divergence for Efficient Reinforcement Learning
Yixian Zhang, Huaze Tang, Changxu Wei +1
The Soft Actor-Critic (SAC) algorithm, a state-of-the-art method in maximum entropy reinforcement learning, traditionally relies on minimizing reverse Kullback-Leibler (KL) diverge…
Policy Newton Algorithm in Reproducing Kernel Hilbert Space
Yixian Zhang, Huaze Tang, Chao Wang +1
Reinforcement learning (RL) policies represented in Reproducing Kernel Hilbert Spaces (RKHS) offer powerful representational capabilities. While second-order optimization methods l…