226 citations · 229 across the 5 of their papers we have counts for
6 papers
SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records
Chengxi Zang, Fei Wang
Contrastive learning has demonstrated promising performance in image and text domains either in a self-supervised or a supervised manner. In this work, we extend the supervised con…
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients
Tingyi Wanyan, Hossein Honarvar, Suraj K. Jaladanki +13
Machine Learning (ML) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major is…
Visualizing Deep Graph Generative Models for Drug Discovery
Karan Yang, Chengxi Zang, Fei Wang
Drug discovery aims at designing novel molecules with specific desired properties for clinical trials. Over past decades, drug discovery and development have been a costly and time…
MoFlow: An Invertible Flow Model for Generating Molecular Graphs
Chengxi Zang, Fei Wang
Generating molecular graphs with desired chemical properties driven by deep graph generative models provides a very promising way to accelerate drug discovery process. Such graph g…
Neural Dynamics on Complex Networks
Chengxi Zang, Fei Wang
Learning continuous-time dynamics on complex networks is crucial for understanding, predicting and controlling complex systems in science and engineering. However, this task is ver…
Structural patterns of information cascades and their implications for dynamics and semantics
Chengxi Zang, Peng Cui, Chaoming Song +2
Information cascades are ubiquitous in both physical society and online social media, taking on large variations in structures, dynamics and semantics. Although the dynamics and se…