Generative Adversarial Networks (GANs) excel at creating realistic images with complex
models for which maximum likelihood is infeasible. However, the convergence of GAN training …

We introduce a modern Hopfield network with continuous states and a corresponding
update rule. The new Hopfield network can store exponentially (with the dimension of the …

CLIP yielded impressive results on zero-shot transfer learning tasks and is considered as a
foundation model like BERT or GPT3. CLIP vision models that have a rich representation are …

A central mechanism in machine learning is to identify, store, and recognize patterns. How
to learn, access, and retrieve such patterns is crucial in Hopfield networks and the more …

Generative adversarial networks (GANs) evolved into one of the most successful unsupervised
techniques for generating realistic images. Even though it has recently been shown that …

We propose a GAN based approach to solve inverse problems which have non-differential
or non-continuous forward relations. In the standard sense, an inverse problem is interpreted …

One of the main tasks of an autonomous agent in a vehicle is to correctly perceive its
environment. Much of the data that needs to be processed is collected by optical sensors such as …

This dissertation estimates factor models with incomplete data. In this context, hidden factors
map serial and cross-sectional correlations of financial and macroeconomic data. The …

Training artificial neural networks is hard. To achieve high predictive capabilities on previously
unseen data, artificial neural networks need a big amount of samples to train on. And it …