10.1k citations · 12.9k across the 89 of their papers we have counts for
15 papers · 1 filter
EventBERT: A Pre-Trained Model for Event Correlation Reasoning
Yucheng Zhou, Xiubo Geng, Tao Shen +2
Event correlation reasoning infers whether a natural language paragraph containing multiple events conforms to human common sense. For example, "Andrew was very drowsy, so he took…
Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision
Yang Li, Guodong Long, Tao Shen +1
Distant supervision uses triple facts in knowledge graphs to label a corpus for relation extraction, leading to wrong labeling and long-tail problems. Some works use the hierarchy…
Sequential Diagnosis Prediction with Transformer and Ontological Representation
Xueping Peng, Guodong Long, Tao Shen +2
Sequential diagnosis prediction on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in the medical domain. EHR data, sequential records of a pati…
Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction
Bo Wang, Tao Shen, Guodong Long +2
Aspect-level sentiment classification (ALSC) aims at identifying the sentiment polarity of a specified aspect in a sentence. ALSC is a practical setting in aspect-based sentiment a…
TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting
Zonghan Wu, Da Zheng, Shirui Pan +3
This paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data. For spatial-t…
Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health
Guodong Long, Tao Shen, Yue Tan +3
Privacy protection is an ethical issue with broad concern in Artificial Intelligence (AI). Federated learning is a new machine learning paradigm to learn a shared model across user…
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…
MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning
Xueping Peng, Guodong Long, Tao Shen +4
Representation learning on electronic health records (EHRs) plays a vital role in downstream medical prediction tasks. Although natural language processing techniques, such as recu…
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…
Task Aligned Generative Meta-learning for Zero-shot Learning
Zhe Liu, Yun Li, Lina Yao +2
Zero-shot learning (ZSL) refers to the problem of learning to classify instances from the novel classes (unseen) that are absent in the training set (seen). Most ZSL methods infer…
Episodic memory governs choices: An RNN-based reinforcement learning model for decision-making task
Xiaohan Zhang, Lu Liu, Guodong Long +2
Typical methods to study cognitive function are to record the electrical activities of animal neurons during the training of animals performing behavioral tasks. A key problem is t…
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…
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
Shaoxiong Ji, Yue Tan, Teemu Saravirta +6
Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, f…