Evolving symbolic density functionals

1 code implementation • 3 Mar 2022 • He Ma, Arunachalam Narayanaswamy, Patrick Riley, Li Li

Systematic development of accurate density functionals has been a decades-long challenge for scientists.

Scientific Discovery by Generating Counterfactuals using Image Translation

1 code implementation • 10 Jul 2020 • Arunachalam Narayanaswamy, Subhashini Venugopalan, Dale R. Webster, Lily Peng, Greg Corrado, Paisan Ruamviboonsuk, Pinal Bavishi, Rory Sayres, Abigail Huang, Siva Balasubramanian, Michael Brenner, Philip Nelson, Avinash V. Varadarajan

Model explanation techniques play a critical role in understanding the source of a model's performance and making its decisions transparent.

Translation

It's easy to fool yourself: Case studies on identifying bias and confounding in bio-medical datasets

no code implementations • 12 Dec 2019 • Subhashini Venugopalan, Arunachalam Narayanaswamy, Samuel Yang, Anton Geraschenko, Scott Lipnick, Nina Makhortova, James Hawrot, Christine Marques, Joao Pereira, Michael Brenner, Lee Rubin, Brian Wainger, Marc Berndl

Confounding variables are a well known source of nuisance in biomedical studies.

BIG-bench Machine Learning

Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

no code implementations • 18 Oct 2018 • Avinash Varadarajan, Pinal Bavishi, Paisan Raumviboonsuk, Peranut Chotcomwongse, Subhashini Venugopalan, Arunachalam Narayanaswamy, Jorge Cuadros, Kuniyoshi Kanai, George Bresnick, Mongkol Tadarati, Sukhum Silpa-archa, Jirawut Limwattanayingyong, Variya Nganthavee, Joe Ledsam, Pearse A. Keane, Greg S. Corrado, Lily Peng, Dale R. Webster

To improve the accuracy of DME screening, we trained a deep learning model to use color fundus photographs to predict ci-DME.

Specificity