32 citations · 36 across the 3 of their papers we have counts for
3 papers
Explaining neural network predictions of material strength
Ian A. Palmer, T. Nathan Mundhenk, Brian Gallagher +1
We recently developed a deep learning method that can determine the critical peak stress of a material by looking at scanning electron microscope (SEM) images of the material's cry…
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…
Generative Counterfactual Introspection for Explainable Deep Learning
Shusen Liu, Bhavya Kailkhura, Donald Loveland +1
In this work, we propose an introspection technique for deep neural networks that relies on a generative model to instigate salient editing of the input image for model interpretat…