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On the Relationship Between Interpretability and Explainability in Machine Learning
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.
Seeking Interpretability and Explainability in Binary Activated Neural Networks
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.
PAC-Bayesian Learning of Aggregated Binary Activated Neural Networks with Probabilities over Representations
Considering a probability distribution over parameters is known as an efficient strategy to learn a neural network with non-differentiable activation functions.
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