no code implementations • 20 Nov 2023 • Benjamin Leblanc, Pascal Germain
Interpretability and explainability have gained more and more attention in the field of machine learning as they are crucial when it comes to high-stakes decisions and troubleshooting.
no code implementations • 7 Sep 2022 • Benjamin Leblanc, Pascal Germain
We study the use of binary activated neural networks as interpretable and explainable predictors in the context of regression tasks on tabular data; more specifically, we provide guarantees on their expressiveness, present an approach based on the efficient computation of SHAP values for quantifying the relative importance of the features, hidden neurons and even weights.
no code implementations • 28 Oct 2021 • Louis Fortier-Dubois, Gaël Letarte, Benjamin Leblanc, François Laviolette, Pascal Germain
Considering a probability distribution over parameters is known as an efficient strategy to learn a neural network with non-differentiable activation functions.