93 citations · 308 across the 25 of their papers we have counts for
10 papers · 1 filter
Improve in-situ life prediction and classification performance by capturing both the present state and evolution rate of battery aging
Mingyuan Zhao, Yongzhi Zhang
This study develops a methodology by capturing both the battery aging state and degradation rate for improved life prediction performance. The aging state is indicated by six physi…
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networks
Sitao Luan, Mingde Zhao, Chenqing Hua +2
The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filters the neighborhood information of nodes…
Revisiting Heterophily For Graph Neural Networks
Sitao Luan, Chenqing Hua, Qincheng Lu +5
Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using graph structures based on the relational inductive bias (homophily assumption). While GNNs have been common…
Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL
Akram Erraqabi, Marlos C. Machado, Mingde Zhao +4
In reinforcement learning, the graph Laplacian has proved to be a valuable tool in the task-agnostic setting, with applications ranging from skill discovery to reward shaping. Rece…
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks
Sitao Luan, Mingde Zhao, Chenqing Hua +2
The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filters the neighborhood node information. Th…
Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks
Sitao Luan, Mingde Zhao, Xiao-Wen Chang +1
The performance limit of Graph Convolutional Networks (GCNs) and the fact that we cannot stack more of them to increase the performance, which we usually do for other deep learning…
META-Learning Eligibility Traces for More Sample Efficient Temporal Difference Learning
Mingde Zhao
Temporal-Difference (TD) learning is a standard and very successful reinforcement learning approach, at the core of both algorithms that learn the value of a given policy, as well…
Semi-Federated Learning
Zhikun Chen, Daofeng Li, Ming Zhao +2
Federated learning (FL) enables massive distributed Information and Communication Technology (ICT) devices to learn a global consensus model without any participants revealing thei…
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Sitao Luan, Mingde Zhao, Xiao-Wen Chang +1
Recently, neural network based approaches have achieved significant improvement for solving large, complex, graph-structured problems. However, their bottlenecks still need to be a…
META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation
Mingde Zhao, Sitao Luan, Ian Porada +2
Temporal-Difference (TD) learning is a standard and very successful reinforcement learning approach, at the core of both algorithms that learn the value of a given policy, as well…