60 citations · 73 across the 5 of their papers we have counts for
5 papers
Decision Theory-Guided Deep Reinforcement Learning for Fast Learning
Zelin Wan, Jin-Hee Cho, Mu Zhu +3
This paper introduces a novel approach, Decision Theory-guided Deep Reinforcement Learning (DT-guided DRL), to address the inherent cold start problem in DRL. By integrating decisi…
Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information
Zhen Guo, Qi Zhang, Xinwei An +6
Due to various and serious adverse impacts of spreading fake news, it is often known that only people with malicious intent would propagate fake news. However, it is not necessaril…
PPO-UE: Proximal Policy Optimization via Uncertainty-Aware Exploration
Qisheng Zhang, Zhen Guo, Audun Jøsang +4
Proximal Policy Optimization (PPO) is a highly popular policy-based deep reinforcement learning (DRL) approach. However, we observe that the homogeneous exploration process in PPO…
A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning
Zhen Guo, Zelin Wan, Qisheng Zhang +7
An in-depth understanding of uncertainty is the first step to making effective decisions under uncertainty. Deep/machine learning (ML/DL) has been hugely leveraged to solve complex…
End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models
Barry Menglong Yao, Aditya Shah, Lichao Sun +2
We propose end-to-end multimodal fact-checking and explanation generation, where the input is a claim and a large collection of web sources, including articles, images, videos, and…