Designing neural networks with bounded Lipschitz constant is a promising way to obtain
certifiably robust classifiers against adversarial examples. However, the relevant progress …

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 …

Many feedforward neural networks (NNs) generate continuous and piecewise-linear
(CPWL) mappings. Specifically, they partition the input domain into regions on which the …

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

Plug-and-Play (PnP) methods are a class of efficient iterative methods that aim to combine
data fidelity terms and deep denoisers using classical optimization algorithms, such as ISTA …

We introduce ABBA networks, a novel class of (almost) nonnegative neural networks, which
are shown to possess a series of appealing properties. In particular, we demonstrate that …

Secure multi-party computation (MPC) techniques can be used to provide data privacy when
users query deep neural network (DNN) models hosted on a public cloud. State-of-the-art …