21 citations · 56 across the 18 of their papers we have counts for
11 papers · 1 filter
Uncovering Promises and Challenges of Federated Learning to Detect Cardiovascular Diseases: A Scoping Literature Review
Sricharan Donkada, Seyedamin Pouriyeh, Reza M. Parizi +4
Cardiovascular diseases (CVD) are the leading cause of death globally, and early detection can significantly improve outcomes for patients. Machine learning (ML) models can help di…
Reinforcement Learning for Generative AI: A Survey
Yuanjiang Cao, Quan Z. Sheng, Julian McAuley +1
Deep Generative AI has been a long-standing essential topic in the machine learning community, which can impact a number of application areas like text generation and computer visi…
BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning
Yunchao Yang, Yipeng Zhou, Miao Hu +2
Federated learning (FL) is a prospective distributed machine learning framework that can preserve data privacy. In particular, cross-silo FL can complete model training by making i…
A Comprehensive Survey on Graph Summarization with Graph Neural Networks
Nasrin Shabani, Jia Wu, Amin Beheshti +5
As large-scale graphs become more widespread, more and more computational challenges with extracting, processing, and interpreting large graph data are being exposed. It is therefo…
State of the Art and Potentialities of Graph-level Learning
Zhenyu Yang, Ge Zhang, Jia Wu +9
Graphs have a superior ability to represent relational data, like chemical compounds, proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as inp…
DAGAD: Data Augmentation for Graph Anomaly Detection
Fanzhen Liu, Xiaoxiao Ma, Jia Wu +7
Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave differently from the benign ones accounting for the majority of graph-structured instances. Rec…
GCN-based Multi-task Representation Learning for Anomaly Detection in Attributed Networks
Venus Haghighi, Behnaz Soltani, Adnan Mahmood +2
Anomaly detection in attributed networks has received a considerable attention in recent years due to its applications in a wide range of domains such as finance, network security,…
Graph-level Neural Networks: Current Progress and Future Directions
Ge Zhang, Jia Wu, Jian Yang +6
Graph-structured data consisting of objects (i.e., nodes) and relationships among objects (i.e., edges) are ubiquitous. Graph-level learning is a matter of studying a collection of…
Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings
Kai Wang, Yu Liu, Quan Z. Sheng
Knowledge graph embedding (KGE) has shown great potential in automatic knowledge graph (KG) completion and knowledge-driven tasks. However, recent KGE models suffer from high train…
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems
Siyu Wang, Yuanjiang Cao, Xiaocong Chen +3
Adversarial attacks, e.g., adversarial perturbations of the input and adversarial samples, pose significant challenges to machine learning and deep learning techniques, including i…
STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
Lei Bai, Lina Yao, Salil. S Kanhere +2
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons is generally chall…