3 papers
cs.LG2026
-Resilience: Model-Free Feature Importance from 1D Topological Signals
Fabian Galis, Darian Onchis, Pedro Real Jurado
We introduce -Resilience, a model-free feature importance method that derives explanations directly from the data itself via 1D topological signals. Our method constructs a clas…
cs.LG2025
ScoresActivation: A New Activation Function for Model Agnostic Global Explainability by Design
Emanuel Covaci, Fabian Galis, Radu Balan +2
Understanding the decision of large deep learning models is a critical challenge for building transparent and trustworthy systems. Although the current post hoc explanation methods…
cs.LG2025
Refining Filter Global Feature Weighting for Fully-Unsupervised Clustering
Fabian Galis, Darian Onchis
In the context of unsupervised learning, effective clustering plays a vital role in revealing patterns and insights from unlabeled data. However, the success of clustering algorith…