Search Results for author:
Found 3 papers, 2 papers with code
Topological Interpretability for Deep-Learning
We infer these features for a given label using a distance metric between probability measures, and demonstrate the stability of our method compared to the LIME and SHAP interpretability methods.
Materials Fingerprinting Classification
Significant progress in many classes of materials could be made with the availability of experimentally-derived large datasets composed of atomic identities and three-dimensional coordinates.
A Stable Cardinality Distance for Topological Classification
This work incorporates topological features via persistence diagrams to classify point cloud data arising from materials science.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
