Search Results for author:
Found 4 papers, 0 papers with code
Targeted Background Removal Creates Interpretable Feature Visualizations
Feature visualization is used to visualize learned features for black box machine learning models.
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust Models
The expansion of explainable artificial intelligence as a field of research has generated numerous methods of visualizing and understanding the black box of a machine learning model.
Transformers in Time-series Analysis: A Tutorial
Transformer architecture has widespread applications, particularly in Natural Language Processing and computer vision.
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks
Later, we discuss how gradient-based methods can be evaluated for their robustness and the role that adversarial robustness plays in having meaningful explanations.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
