Rapidly increasing quality of AI-generated content makes it difficult to distinguish between
human and AI-generated texts, which may lead to undesirable consequences for society …

Electric vehicles in a short time will make up the majority of the fleet of vehicles used in
general. This state of affairs will generate huge sets of data, which can be further …

In recent years there has been increased interest in understanding the interplay between
deep generative models (DGMs) and the manifold hypothesis. Research in this area focuses …

We present the results of the" Fast Calorimeter Simulation Challenge 2022"-the
CaloChallenge. We study state-of-the-art generative models on four calorimeter shower …

Likelihood-based deep generative models (DGMs) commonly exhibit a puzzling behaviour:
when trained on a relatively complex dataset, they assign higher likelihood values to out-of …

In neural networks, task-relevant information is represented jointly by groups of neurons.
However, the specific way in which this mutual information about the classification label is …

Background The advent of next-generation computed tomography (CT)-and magnetic
resonance imaging (MRI) opened many new perspectives in the evaluation of tumor …

Most of the existing methods for estimating the local intrinsic dimension of a data distribution
do not scale well to high dimensional data. Many of them rely on a non-parametric nearest …

Domain adaptation is a popular paradigm in modern machine learning which aims at
tackling the problem of divergence (or shift) between the labeled training and validation …

Abstract The NOD-Like Receptor Protein-3 (NLRP3) inflammasome is a key therapeutic
target for the treatment of epilepsy and has been reported to regulate inflammation in …