Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold
Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs …

The detection of weld defects by using X-rays is an important task in the industry. It requires
trained specialists with the expertise to conduct a timely inspection, which is costly and …

We develop a variational framework to understand the properties of functions learned by
fitting deep neural networks with rectified linear unit (ReLU) activations to data. We propose …

In the past decade, deep learning became the prevalent methodology for predictive
modeling thanks to the remarkable accuracy of deep neural networks in tasks such as …

Neural networks have proven to be a highly effective tool for solving complex problems in
many areas of life. Recently, their importance and practical usability have further been …

We propose to learn non-convex regularizers with a prescribed upper bound on their weak-
convexity modulus. Such regularizers give rise to variational denoisers that minimize a …

The emergence of deep-learning-based methods to solve image-reconstruction problems
has enabled a significant increase in quality. Unfortunately, these new methods often lack …

Lipschitz-constrained neural networks have several advantages over unconstrained ones
and can be applied to a variety of problems, making them a topic of attention in the deep …

Next-generation radio interferometers like the Square Kilometer Array have the potential to
unlock scientific discoveries thanks to their unprecedented angular resolution and …

In this paper we present ExSpliNet, an interpretable and expressive neural network model.
The model combines ideas of Kolmogorov neural networks, ensembles of probabilistic trees …