32 citations · 46 across the 10 of their papers we have counts for
9 papers
Reliable Graph Neural Network Explanations Through Adversarial Training
Donald Loveland, Shusen Liu, Bhavya Kailkhura +2
Graph neural network (GNN) explanations have largely been facilitated through post-hoc introspection. While this has been deemed successful, many post-hoc explanation methods have…
Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design
Shusen Liu, Bhavya Kailkhura, Jize Zhang +4
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges. However, despite the effectiveness in buildin…
Actionable Attribution Maps for Scientific Machine Learning
Shusen Liu, Bhavya Kailkhura, Jize Zhang +4
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges. However, despite the effectiveness in buildin…
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets
Sam Ade Jacobs, Brian Van Essen, David Hysom +11
Training deep neural networks on large scientific data is a challenging task that requires enormous compute power, especially if no pre-trained models exist to initialize the proce…
Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion
Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu +2
There is significant interest in using modern neural networks for scientific applications due to their effectiveness in modeling highly complex, non-linear problems in a data-drive…
Function Preserving Projection for Scalable Exploration of High-Dimensional Data
Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan +1
We present function preserving projections (FPP), a scalable linear projection technique for discovering interpretable relationships in high-dimensional data. Conventional dimensio…