1.9k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Google (United States)US2 papers
- California Pacific Medical CenterUS1 paper
- Eindhoven University of TechnologyNL1 paper
- Google (Israel)IL1 paper
- Harvard UniversityUS1 paper
- Icahn School of Medicine at Mount SinaiUS1 paper
- Mountain View CollegeUS1 paper
- National University of SingaporeSG1 paper
- Stanford MedicineUS1 paper
- Stanford UniversityUS1 paper
- University of PennsylvaniaUS1 paper
- Weizmann Institute of ScienceIL1 paper
6 papers
TRILLsson: Distilled Universal Paralinguistic Speech Representations
Joel Shor, Subhashini Venugopalan
Recent advances in self-supervision have dramatically improved the quality of speech representations. However, deployment of state-of-the-art embedding models on devices has been r…
Universal Paralinguistic Speech Representations Using Self-Supervised Conformers
Joel Shor, Aren Jansen, Wei Han +2
Many speech applications require understanding aspects beyond the words being spoken, such as recognizing emotion, detecting whether the speaker is wearing a mask, or distinguishin…
Deep Unfolding with Normalizing Flow Priors for Inverse Problems
Xinyi Wei, Hans van Gorp, Lizeth Gonzalez Carabarin +3
Many application domains, spanning from computational photography to medical imaging, require recovery of high-fidelity images from noisy, incomplete or partial/compressed measurem…
Doubly Robust Regression Analysis for Data Fusion
Katherine Evans, BaoLuo Sun, James Robins +1
This paper investigates the problem of making inference about a parametric model for the regression of an outcome variable on covariates when data are fused from two se…
Confounding variables can degrade generalization performance of radiological deep learning models
John R. Zech, Marcus A. Badgeley, Manway Liu +3
Early results in using convolutional neural networks (CNNs) on x-rays to diagnose disease have been promising, but it has not yet been shown that models trained on x-rays from one…
Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning
Ryan Poplin, Avinash V. Varadarajan, Katy Blumer +5
Traditionally, medical discoveries are made by observing associations and then designing experiments to test these hypotheses. However, observing and quantifying associations in im…