7 citations · 9 across the 4 of their papers we have counts for
4 papers
Unifying Attribution-Based Explanations Using Functional Decomposition
Arne Gevaert, Yvan Saeys
The black box problem in machine learning has led to the introduction of an ever-increasing set of explanation methods for complex models. These explanations have different propert…
Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems
Arne Gevaert, Jonathan Peck, Yvan Saeys
Deep Reinforcement Learning uses a deep neural network to encode a policy, which achieves very good performance in a wide range of applications but is widely regarded as a black bo…
PDD-SHAP: Fast Approximations for Shapley Values using Functional Decomposition
Arne Gevaert, Yvan Saeys
Because of their strong theoretical properties, Shapley values have become very popular as a way to explain predictions made by black box models. Unfortuately, most existing techni…
Evaluating Feature Attribution Methods in the Image Domain
Arne Gevaert, Axel-Jan Rousseau, Thijs Becker +3
Feature attribution maps are a popular approach to highlight the most important pixels in an image for a given prediction of a model. Despite a recent growth in popularity and avai…