104 citations · 298 across the 20 of their papers we have counts for
5 papers · 1 filter
Dynamical Wasserstein Barycenters for Time-series Modeling
Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes +1
Many time series can be modeled as a sequence of segments representing high-level discrete states, such as running and walking in a human activity application. Flexible models shou…
A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms
Zhe Huang, Gary Long, Benjamin Wessler +1
Semi-supervised image classification has shown substantial progress in learning from limited labeled data, but recent advances remain largely untested for clinical applications. Mo…
Evaluating the Use of Reconstruction Error for Novelty Localization
Patrick Feeney, Michael C. Hughes
The pixelwise reconstruction error of deep autoencoders is often utilized for image novelty detection and localization under the assumption that pixels with high error indicate whi…
Stochastic Iterative Graph Matching
Linfeng Liu, Michael C. Hughes, Soha Hassoun +1
Recent works leveraging Graph Neural Networks to approach graph matching tasks have shown promising results. Recent progress in learning discrete distributions poses new opportunit…
Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories
Gian Marco Visani, Alexandra Hope Lee, Cuong Nguyen +4
We address the problem of modeling constrained hospital resources in the midst of the COVID-19 pandemic in order to inform decision-makers of future demand and assess the societal…