Search Results for author:
Found 5 papers, 2 papers with code
Global explainability of a deep abstaining classifier
We present a global explainability method to characterize sources of errors in the histology prediction task of our real-world multitask convolutional neural network (MTCNN)-based deep abstaining classifier (DAC), for automated annotation of cancer pathology reports from NCI-SEER registries.
Investigating the importance of social vulnerability in opioid-related mortality across the United States
The opioid crisis remains a critical public health challenge in the United States.
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.
