David Vigouroux

Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis

2022 · arXiv (Cornell University)

A variety of methods have been proposed to try to explain how deep neural networks make their decisions. Key to those approaches is the need to sample the pixel space efficiently in order to derive …

CRAFT: Concept Recursive Activation FacTorization for Explainability

2022 · arXiv (Cornell University)

Attribution methods, which employ heatmaps to identify the most influential regions of an image that impact model decisions, have gained widespread popularity as a type of explainability method. However, recent research has exposed the limited …