Progress in making neural networks more robust against adversarial attacks is mostly
marginal, despite the great efforts of the research community. Moreover, the robustness …

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 …

Although Lipschitz-constrained neural networks have many applications in machine
learning, the design and training of expressive Lipschitz-constrained networks is very …

The reliability of neural networks is essential for their use in safety-critical applications.
Existing approaches generally aim at improving the robustness of neural networks to either …

Computational imaging has been revolutionized by compressed sensing algorithms, which
offer guaranteed uniqueness, convergence, and stability properties. Model-based deep …

We establish an equivalence between a family of adversarial training problems for non-
parametric binary classification and a family of regularized risk minimization problems where …

Current research in adversarial robustness of LLMs focuses on discrete input manipulations
in the natural language space, which can be directly transferred to closed-source models …

Fast and precise Lipschitz constant estimation of neural networks is an important task for
deep learning. Researchers have recently found an intrinsic trade-off between the accuracy …

We investigate numerous structural connections between numerical algorithms for partial
differential equations (PDEs) and neural architectures. Our goal is to transfer the rich set of …

Learned inverse problem solvers exhibit remarkable performance in applications like image
reconstruction tasks. These data-driven reconstruction methods often follow a two-step …