254 citations · 304 across the 20 of their papers we have counts for
7 papers · 1 filter
Adjacency constraint for efficient hierarchical reinforcement learning
Tianren Zhang, Shangqi Guo, Tian Tan +2
Goal-conditioned Hierarchical Reinforcement Learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training in…
Non-autoregressive Transformer with Unified Bidirectional Decoder for Automatic Speech Recognition
Chuan-Fei Zhang, Yan Liu, Tian-Hao Zhang +3
Non-autoregressive (NAR) transformer models have been studied intensively in automatic speech recognition (ASR), and a substantial part of NAR transformer models is to use the casu…
Fairness-Aware Online Meta-learning
Chen Zhao, Feng Chen, Bhavani Thuraisingham
In contrast to offline working fashions, two research paradigms are devised for online learning: (1) Online Meta Learning (OML) learns good priors over model parameters (or learnin…
Network-wide link travel time and station waiting time estimation using automatic fare collection data: A computational graph approach
Jinlei Zhang, Feng Chen, Lixing Yang +3
Urban rail transit (URT) system plays a dominating role in many megacities like Beijing and Hong Kong. Due to its important role and complex nature, it is always in great need for…
A method of supervised learning from conflicting data with hidden contexts
Tianren Zhang, Yizhou Jiang, Feng Chen
Conventional supervised learning assumes a stable input-output relationship. However, this assumption fails in open-ended training settings where the input-output relationship depe…
CRIL: Continual Robot Imitation Learning via Generative and Prediction Model
Chongkai Gao, Haichuan Gao, Shangqi Guo +2
Imitation learning (IL) algorithms have shown promising results for robots to learn skills from expert demonstrations. However, they need multi-task demonstrations to be provided a…
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
Zhiqian Chen, Fanglan Chen, Lei Zhang +7
Deep learning's performance has been extensively recognized recently. Graph neural networks (GNNs) are designed to deal with graph-structural data that classical deep learning does…