Change-of-variables (CoV) formulas allow to reduce complicated probability densities to
simpler ones by a learned transformation with tractable Jacobian determinant. They are thus …

Dynamical systems with extreme events are difficult to capture with data-driven modeling
due to the relative scarcity of data within extreme events compared to the typical dynamics of …

In this work, we present and study Continuous Generative Neural Networks (CGNNs),
namely, generative models in the continuous setting: the output of a CGNN belongs to an …

The manifold hypothesis posits that high-dimensional data often lies on a lower-dimensional
manifold and that utilizing this manifold as the target space yields more efficient …

In this work, we present and study Continuous Generative Neural Networks (CGNNs),
namely, generative models in the continuous setting: the output of a CGNN belongs to an …

The expanding research on manifold-based self-supervised learning (SSL) builds on the
manifold hypothesis, which suggests that the inherent complexity of high-dimensional data …

In this thesis we investigate two distinct regularizing approaches for solving inverse
problems. The first approach involves assuming that the unknown belongs to a manifold …

Multi-view or multi-modal learning, in theory, should enhance clustering results by
leveraging information from other modalities. However, it is commonly observed that …

Background We are given data points x1,..., xN∈ Rn and assume that the elements of the
datasets are located on an embedded d-dimensional submanifold M⊆ Rn with d≪ n, which …