305 citations · 420 across the 5 of their papers we have counts for
5 papers
Deployment of a Robust and Explainable Mortality Prediction Model: The COVID-19 Pandemic and Beyond
Jacob R. Epifano, Stephen Glass, Ravi P. Ramachandran +3
This study investigated the performance, explainability, and robustness of deployed artificial intelligence (AI) models in predicting mortality during the COVID-19 pandemic and bey…
Targeted Background Removal Creates Interpretable Feature Visualizations
Ian E. Nielsen, Erik Grundeland, Joseph Snedeker +2
Feature visualization is used to visualize learned features for black box machine learning models. Our approach explores an altered training process to improve interpretability of…
Revisiting the Fragility of Influence Functions
Jacob R. Epifano, Ravi P. Ramachandran, Aaron J. Masino +1
In the last few years, many works have tried to explain the predictions of deep learning models. Few methods, however, have been proposed to verify the accuracy or faithfulness of…
Transformers in Time-series Analysis: A Tutorial
Sabeen Ahmed, Ian E. Nielsen, Aakash Tripathi +3
Transformer architecture has widespread applications, particularly in Natural Language Processing and computer vision. Recently Transformers have been employed in various aspects o…
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks
Ian E. Nielsen, Dimah Dera, Ghulam Rasool +2
With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decis…