21 citations · 56 across the 18 of their papers we have counts for
9 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…
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…