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Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design

Jul 16, 2020
Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, T. Yong-Jin Han


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Actionable Attribution Maps for Scientific Machine Learning

Jun 30, 2020
Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, T. Yong-Jin Han


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Parallelizing Training of Deep Generative Models on Massive Scientific Datasets

Oct 05, 2019
Sam Ade Jacobs, Brian Van Essen, David Hysom, Jae-Seung Yeom, Tim Moon, Rushil Anirudh, Jayaraman J. Thiagaranjan, Shusen Liu, Peer-Timo Bremer, Jim Gaffney, Tom Benson, Peter Robinson, Luc Peterson, Brian Spears


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Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion

Oct 03, 2019
Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu, Peer-Timo Bremer, Brian K. Spears

* Machine Learning for Physical Sciences Workshop at NeurIPS 2019 

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Function Preserving Projection for Scalable Exploration of High-Dimensional Data

Sep 25, 2019
Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer


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Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications

Jul 19, 2019
Shusen Liu, Di Wang, Dan Maljovec, Rushil Anirudh, Jayaraman J. Thiagarajan, Sam Ade Jacobs, Brian C. Van Essen, David Hysom, Jae-Seung Yeom, Jim Gaffney, Luc Peterson, Peter B. Robinson, Harsh Bhatia, Valerio Pascucci, Brian K. Spears, Peer-Timo Bremer


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Generative Counterfactual Introspection for Explainable Deep Learning

Jul 06, 2019
Shusen Liu, Bhavya Kailkhura, Donald Loveland, Yong Han


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Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections

Dec 20, 2017
Jayaraman J. Thiagarajan, Shusen Liu, Karthikeyan Natesan Ramamurthy, Peer-Timo Bremer


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