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Greg S. Corrado
Researcher at Google
Publications - 149
Citations - 114561
Greg S. Corrado is an academic researcher from Google. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 54, co-authored 125 publications receiving 95930 citations. Previous affiliations of Greg S. Corrado include IBM & Howard Hughes Medical Institute.
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Predicting Progression of Age-related Macular Degeneration from Fundus Images using Deep Learning
Boris Babenko,Siva Balasubramanian,Katy Blumer,Greg S. Corrado,Lily Peng,Dale R. Webster,Naama Hammel,Avinash V. Varadarajan +7 more
TL;DR: A deep learning algorithm is developed and validated to predict 1-year progression of eyes with no, early, or intermediate AMD to nvAMD, using color fundus photographs (CFP), and performs better in predicting progression to n vAMD than manual grades.
Proceedings ArticleDOI
Explaining an increase in predicted risk for clinical alerts
Michaela Hardt,Alvin Rajkomar,Gerardo Flores,Andrew M. Dai,Michael D. Howell,Greg S. Corrado,Claire Cui,Moritz Hardt +7 more
TL;DR: In this paper, the authors consider explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estimates given an input at each time step, and the goal of the explanation is to attribute the increase to a few relevant inputs from the past.
TensorFlow.js: Machine Learning for the Web and Beyond
Daniel Smilkov,Nikhil Thorat,Yannick Assogba,Ann Yuan,Nick Kreeger,Ping Yu,Kangyi Zhang,Shanqing Cai,Eric Nielsen,David A W Soergel,Stan Bileschi,Michael Terry,Charles Nicholson,Sandeep N. Gupta,Sarah Sirajuddin,D. Sculley,Rajat Monga,Greg S. Corrado,Fernanda B. Viégas,Martin Wattenberg +19 more
TL;DR: TensorFlow.js as discussed by the authors is a library for building and executing machine learning algorithms in JavaScript, running in a web browser and in the Node.js environment, allowing models to be ported between the Python and JavaScript ecosystems.
Book ChapterDOI
Scientific Discovery by Generating Counterfactuals Using Image Translation
Arunachalam Narayanaswamy,Subhashini Venugopalan,Dale R. Webster,Lily Peng,Greg S. Corrado,Paisan Ruamviboonsuk,Pinal Bavishi,Michael Brenner,Philip C. Nelson,Avinash V. Varadarajan +9 more
TL;DR: In this paper, the authors propose a framework to convert predictions from explanation techniques to a mechanism of discovery, which is able to explain the underlying scientific mechanism, thus bridging the gap between the model's performance and human understanding.