21 citations · 56 across the 18 of their papers we have counts for
18 papers
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…
Separate-and-Aggregate: A Transformer-based Patch Refinement Model for Knowledge Graph Completion
Chen Chen, Yufei Wang, Yang Zhang +2
Knowledge graph completion (KGC) is the task of inferencing missing facts from any given knowledge graphs (KG). Previous KGC methods typically represent knowledge graph entities an…
ProcessGPT: Transforming Business Process Management with Generative Artificial Intelligence
Amin Beheshti, Jian Yang, Quan Z. Sheng +6
Generative Pre-trained Transformer (GPT) is a state-of-the-art machine learning model capable of generating human-like text through natural language processing (NLP). GPT is traine…
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…
Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation
Siyu Wang, Xiaocong Chen, Quan Z. Sheng +2
Recommendation models are typically trained on observational user interaction data, but the interactions between latent factors in users' decision-making processes lead to complex…
Guided Image-to-Image Translation by Discriminator-Generator Communication
Yuanjiang Cao, Lina Yao, Le Pan +2
The goal of Image-to-image (I2I) translation is to transfer an image from a source domain to a target domain, which has recently drawn increasing attention. One major branch of thi…
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,…
A Survey on Participant Selection for Federated Learning in Mobile Networks
Behnaz Soltani, Venus Haghighi, Adnan Mahmood +2
Federated Learning (FL) is an efficient distributed machine learning paradigm that employs private datasets in a privacy-preserving manner. The main challenges of FL is that end de…
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…
A Survey on Location-Driven Influence Maximization
Taotao Cai, Quan Z. Sheng, Xiangyu Song +5
Influence Maximization (IM), which aims to select a set of users from a social network to maximize the expected number of influenced users, is an evergreen hot research topic. Its…
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…
Graph Learning based Recommender Systems: A Review
Shoujin Wang, Liang Hu, Yan Wang +6
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph learning approaches to model u…
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…