Pantypes: Diverse Representatives for Self-Explainable Models

1 code implementation • 14 Mar 2024 • Rune Kjærsgaard, Ahcène Boubekki, Line Clemmensen

Prototypical self-explainable classifiers have emerged to meet the growing demand for interpretable AI systems.

Explainable Models Fairness

RELAX: Representation Learning Explainability

1 code implementation • 19 Dec 2021 • Kristoffer K. Wickstrøm, Daniel J. Trosten, Sigurd Løkse, Ahcène Boubekki, Karl Øyvind Mikalsen, Michael C. Kampffmeyer, Robert Jenssen

Our approach can also model the uncertainty in its explanations, which is essential to produce trustworthy explanations.

Representation Learning

Joint Optimization of an Autoencoder for Clustering and Embedding

1 code implementation • 7 Dec 2020 • Ahcène Boubekki, Michael Kampffmeyer, Robert Jenssen, Ulf Brefeld

That simple neural network, referred to as the clustering module, can be integrated into a deep autoencoder resulting in a deep clustering model able to jointly learn a clustering and an embedding.

Clustering Deep Clustering

Frame-based Data Factorizations

no code implementations • ICML 2017 • Sebastian Mair, Ahcène Boubekki, Ulf Brefeld

Archetypal Analysis is the method of choice to compute interpretable matrix factorizations.