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AB-Training: A Communication-Efficient Approach for Distributed Low-Rank Learning
Communication bottlenecks hinder the scalability of distributed neural network training, particularly on distributed-memory computing clusters.
Harnessing Orthogonality to Train Low-Rank Neural Networks
This study explores the learning dynamics of neural networks by analyzing the singular value decomposition (SVD) of their weights throughout training.
A dynamic risk score for early prediction of cardiogenic shock using machine learning
We developed a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict onset of cardiogenic shock.
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