34 citations · 89 across the 18 of their papers we have counts for
4 papers · 1 filter
Scale-Adaptive Power Flow Analysis with Local Topology Slicing and Multi-Task Graph Learning
Yongzhe Li, Lin Guan, Zihan Cai +3
Developing deep learning models with strong adaptability to topological variations is of great practical significance for power flow analysis. To enhance model performance under va…
Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers
Lin Guan, Xia Xiao, Ming Chen +1
Feature selection has been an essential step in developing industry-scale deep Click-Through Rate (CTR) prediction systems. The goal of neural feature selection (NFS) is to choose…
Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning
Yantian Zha, Lin Guan, Subbarao Kambhampati
Our work aims at efficiently leveraging ambiguous demonstrations for the training of a reinforcement learning (RL) agent. An ambiguous demonstration can usually be interpreted in m…
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset
Ruohan Zhang, Calen Walshe, Zhuode Liu +6
Large-scale public datasets have been shown to benefit research in multiple areas of modern artificial intelligence. For decision-making research that requires human data, high-qua…