226 citations · 248 across the 6 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…
A(DP)SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy
Jie Xu, Wei Zhang, Fei Wang
As deep learning models are usually massive and complex, distributed learning is essential for increasing training efficiency. Moreover, in many real-world application scenarios li…
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…
Identifying Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks
Zhenxing Xu, Jingyuan Chou, Xi Sheryl Zhang +10
Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the rapid loss of kidney excretory function, which aggravates the clinical severity of other diseases in a…
Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Study
Suyog Gupta, Wei Zhang, Fei Wang
This paper presents Rudra, a parameter server based distributed computing framework tuned for training large-scale deep neural networks. Using variants of the asynchronous stochast…