104 citations · 298 across the 20 of their papers we have counts for
22 papers
SINCERE: Supervised Information Noise-Contrastive Estimation REvisited
Patrick Feeney, Michael C. Hughes
The information noise-contrastive estimation (InfoNCE) loss function provides the basis of many self-supervised deep learning methods due to its strong empirical results and theore…
Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning
Zhe Huang, Benjamin S. Wessler, Michael C. Hughes
Aortic stenosis (AS) is a degenerative valve condition that causes substantial morbidity and mortality. This condition is under-diagnosed and under-treated. In clinical practice, A…
Nonparametric and Regularized Dynamical Wasserstein Barycenters for Sequential Observations
Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes +1
We consider probabilistic models for sequential observations which exhibit gradual transitions among a finite number of states. We are particularly motivated by applications such a…
Fix-A-Step: Semi-supervised Learning from Uncurated Unlabeled Data
Zhe Huang, Mary-Joy Sidhom, Benjamin S. Wessler +1
Semi-supervised learning (SSL) promises improved accuracy compared to training classifiers on small labeled datasets by also training on many unlabeled images. In real applications…
Easy Variational Inference for Categorical Models via an Independent Binary Approximation
Michael T. Wojnowicz, Shuchin Aeron, Eric L. Miller +1
We pursue tractable Bayesian analysis of generalized linear models (GLMs) for categorical data. Thus far, GLMs are difficult to scale to more than a few dozen categories due to non…
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…