10.1k citations · 12.1k across the 55 of their papers we have counts for
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Generalization in Text-based Games via Hierarchical Reinforcement Learning
Yunqiu Xu, Meng Fang, Ling Chen +2
Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents. However, the general…
Federated Learning for Open Banking
Guodong Long, Yue Tan, Jing Jiang +1
Open banking enables individual customers to own their banking data, which provides fundamental support for the boosting of a new ecosystem of data marketplaces and financial servi…
Multi-Center Federated Learning: Clients Clustering for Better Personalization
Guodong Long, Ming Xie, Tao Shen +4
Personalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent cl…
Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filtering
Zonghan Wu, Shirui Pan, Guodong Long +2
Graph convolutional networks are becoming indispensable for deep learning from graph-structured data. Most of the existing graph convolutional networks share two big shortcomings.…
FedProto: Federated Prototype Learning across Heterogeneous Clients
Yue Tan, Guodong Long, Lu Liu +4
Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occur…
Isometric Propagation Network for Generalized Zero-shot Learning
Lu Liu, Tianyi Zhou, Guodong Long +3
Zero-shot learning (ZSL) aims to classify images of an unseen class only based on a few attributes describing that class but no access to any training sample. A popular strategy is…