Year: 2025
Type: preprint
Abstract: CEBRA has favorable properties for identifying the Jacobian matrix of the data generating process. Empirically, we demonstrate robust approximation of zero vs. non-zero entries in the ground-truth attribution map on synthetic datasets, and significant improvements across previous attribution methods based on feature ablation, Shapley values, and other gradient-based methods. Our work constitutes a first example of identifiable inference of time-series attribution maps and opens avenues to a better understanding of time-series data, such as for neural dynamics and decision-processes within neural networks.
Source: PubMed
Authors: Schneider, Steffen, Laiz, Rodrigo González, Filippova, Anastasiia, Frey, Markus, Mathis, Mackenzie Weygandt
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Institutes of Health
OpenAlex ID: W6929576121