2 citations · 2 across the 2 of their papers we have counts for
4 papers
Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics
Conor F. Hayes, Felipe Leno Da Silva, Jiachen Yang +15
Deep Symbolic Optimization (DSO) is a novel computational framework that enables symbolic optimization for scientific discovery, particularly in applications involving the search f…
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…
Efficient Saliency Maps for Explainable AI
T. Nathan Mundhenk, Barry Y. Chen, Gerald Friedland
We describe an explainable AI saliency map method for use with deep convolutional neural networks (CNN) that is much more efficient than popular fine-resolution gradient methods. I…
Predicting Compressive Strength of Consolidated Molecular Solids Using Computer Vision and Deep Learning
Brian Gallagher, Matthew Rever, Donald Loveland +6
We explore the application of computer vision and machine learning (ML) techniques to predict material properties (e.g. compressive strength) based on SEM images. We show that it's…