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20202023
most citedParameterized Explainer for Graph Neural Network

213 citations · 346 across the 19 of their papers we have counts for

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5 papers · 1 filter

cs.LG20236 cited

Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations

Tianxiang Zhao, Wenchao Yu, Suhang Wang +6

Imitation learning has achieved great success in many sequential decision-making tasks, in which a neural agent is learned by imitating collected human demonstrations. However, exi…

cs.LG2022

Personalized Federated Learning via Heterogeneous Modular Networks

Tianchun Wang, Wei Cheng, Dongsheng Luo +5

Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention. Despite…

cs.LG2022

Deep Federated Anomaly Detection for Multivariate Time Series Data

Wei Zhu, Dongjin Song, Yuncong Chen +6

Despite the fact that many anomaly detection approaches have been developed for multivariate time series data, limited effort has been made on federated settings in which multivari…

cs.LG202132 cited

InfoGCL: Information-Aware Graph Contrastive Learning

Dongkuan Xu, Wei Cheng, Dongsheng Luo +2

Various graph contrastive learning models have been proposed to improve the performance of learning tasks on graph datasets in recent years. While effective and prevalent, these mo…

cs.LG2020213 cited

Parameterized Explainer for Graph Neural Network

Dongsheng Luo, Wei Cheng, Dongkuan Xu +4

Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by GNNs remains a challenging open problem. The leading method independently addresses the loca…