Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Abstract Interest in Machine Learning applications to tackle clinical and biological problems
is increasing. This is driven by promising results reported in many research papers, the …

In this paper we present TruFor, a forensic framework that can be applied to a large variety
of image manipulation methods, from classic cheapfakes to more recent manipulations …

Clusters of viral pneumonia occurrences over a short period may be a harbinger of an
outbreak or pandemic. Rapid and accurate detection of viral pneumonia using chest X-rays …

Large language models (LLMs) specializing in natural language generation (NLG) have
recently started exhibiting promising capabilities across a variety of domains. However …

Leveraging physical knowledge described by partial differential equations (PDEs) is an
appealing way to improve unsupervised video forecasting models. Since physics is too …

Despite the power of deep neural networks for a wide range of tasks, an overconfident
prediction issue has limited their practical use in many safety-critical applications. Many …

Integration of heterogeneous and high-dimensional data (eg, multiomics) is becoming
increasingly important. Existing multimodal classification algorithms mainly focus on …

Classification on long-tailed distributed data is a challenging problem, which suffers from
serious class-imbalance and accordingly unpromising performance especially on tail …

It has been a hot research topic to enable machines to understand human emotions in
multimodal contexts under dialogue scenarios, which is tasked with multimodal emotion …