10.1k citations · 12.1k across the 55 of their papers we have counts for
10 papers · 1 filter
Towards Coarse and Fine-grained Multi-Graph Multi-Label Learning
Yejiang Wang, Yuhai Zhao, Zhengkui Wang +1
Multi-graph multi-label learning (\textsc{Mgml}) is a supervised learning framework, which aims to learn a multi-label classifier from a set of labeled bags each containing a numbe…
Cooperative Heterogeneous Deep Reinforcement Learning
Han Zheng, Pengfei Wei, Jing Jiang +3
Numerous deep reinforcement learning agents have been proposed, and each of them has its strengths and flaws. In this work, we present a Cooperative Heterogeneous Deep Reinforcemen…
Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention
Yang Li, Tao Shen, Guodong Long +3
Wrong labeling problem and long-tail relations are two main challenges caused by distant supervision in relation extraction. Recent works alleviate the wrong labeling by selective…
RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion
Hao Huang, Guodong Long, Tao Shen +2
Many graph embedding approaches have been proposed for knowledge graph completion via link prediction. Among those, translating embedding approaches enjoy the advantages of light-w…
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games
Yunqiu Xu, Meng Fang, Ling Chen +3
We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language. While different methods have been developed to repr…
BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes
Xueping Peng, Guodong Long, Tao Shen +3
Electronic health records (EHRs) are longitudinal records of a patient's interactions with healthcare systems. A patient's EHR data is organized as a three-level hierarchy from top…
Attribute Propagation Network for Graph Zero-shot Learning
Lu Liu, Tianyi Zhou, Guodong Long +2
The goal of zero-shot learning (ZSL) is to train a model to classify samples of classes that were not seen during training. To address this challenging task, most ZSL methods relat…
Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy
Lu Liu, Tianyi Zhou, Guodong Long +2
We study many-class few-shot (MCFS) problem in both supervised learning and meta-learning settings. Compared to the well-studied many-class many-shot and few-class few-shot problem…
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Zonghan Wu, Shirui Pan, Guodong Long +3
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumptio…
Multi-Center Federated Learning: Clients Clustering for Better Personalization
Guodong Long, Ming Xie, Tao Shen +4
Federated learning has received great attention for its capability to train a large-scale model in a decentralized manner without needing to access user data directly. It helps pro…