Interpretability and explainability are crucial for machine learning (ML) and statistical
applications in medicine, economics, law, and natural sciences and form an essential …

Conventional neural network elastoplasticity models are often perceived as lacking
interpretability. This paper introduces a two-step machine learning approach that returns …

Although deep learning has achieved remarkable success in various scientific machine
learning applications, its opaque nature poses concerns regarding interpretability and …

Abstract Neural Ordinary Differential Equations (NODEs) have proven successful in learning
dynamical systems in terms of accurately recovering the observed trajectories. While …

Abstract Generalized Additive Models (GAMs) have recently experienced a resurgence in
popularity due to their interpretability, which arises from expressing the target value as a …

Abstract Generalized Additive Models (GAMs) are a family of flexible and interpretable
models with old roots in statistics. GAMs are often used with pairwise interactions to improve …

Neural networks have achieved remarkable success across various fields. However, the
lack of interpretability limits their practical use, particularly in critical decision-making …

Deep neural networks (DNNs) have found successful applications in many fields, but their
black-box nature hinders interpretability. This is addressed by the neural additive model …

Clinical databases typically include, for each patient, many heterogeneous features, for
example blood exams, the clinical history before the onset of the disease, the evolution of …

Differential learning rate (DLR), a technique that applies different learning rates to different
model parameters, has been widely used in deep learning and achieved empirical success …