75 citations · 188 across the 7 of their papers we have counts for
7 papers
E2E-AT: A Unified Framework for Tackling Uncertainty in Task-aware End-to-end Learning
Wangkun Xu, Jianhong Wang, Fei Teng
Successful machine learning involves a complete pipeline of data, model, and downstream applications. Instead of treating them separately, there has been a prominent increase of at…
Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognition
Weishan Ye, Zhiguo Zhang, Fei Teng +6
Electroencephalography (EEG) is an objective tool for emotion recognition with promising applications. However, the scarcity of labeled data remains a major challenge in this field…
Blending Data and Physics Against False Data Injection Attack: An Event-Triggered Moving Target Defence Approach
Wangkun Xu, Martin Higgins, Jianhong Wang +2
Fast and accurate detection of cyberattacks is a key element for a cyber-resilient power system. Recently, data-driven detectors and physics-based Moving Target Defences (MTD) have…
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks
Jianhong Wang, Wangkun Xu, Yunjie Gu +2
This paper presents a problem in power networks that creates an exciting and yet challenging real-world scenario for application of multi-agent reinforcement learning (MARL). The e…
Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System
Jianhong Wang, Yuan Zhang, Tae-Kyun Kim +1
Designing task-oriented dialogue systems is a challenging research topic, since it needs not only to generate utterances fulfilling user requests but also to guarantee the comprehe…
Shapley Q-value: A Local Reward Approach to Solve Global Reward Games
Jianhong Wang, Yuan Zhang, Tae-Kyun Kim +1
Cooperative game is a critical research area in the multi-agent reinforcement learning (MARL). Global reward game is a subclass of cooperative games, where all agents aim to maximi…
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
Rui Luo, Jianhong Wang, Yaodong Yang +2
We propose a new sampling method, the thermostat-assisted continuously-tempered Hamiltonian Monte Carlo, for Bayesian learning on large datasets and multimodal distributions. It si…