Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …

Graph Neural Networks (GNNs) have been extensively used in various real-world
applications. However, the predictive uncertainty of GNNs stemming from diverse sources …

Automatic emotion recognition based on multichannel Electroencephalography (EEG) holds
great potential in advancing human-computer interaction. However, several significant …

As phasor measurement units (PMUs) become more widely used in transmission power
systems, a fast state estimation (SE) algorithm that can take advantage of their high sample …

Graph classification in medical imaging and drug discovery requires accuracy and robust
uncertainty quantification. To address this need, we introduce Conditional Prediction ROC …

Graphical models, including Graph Neural Networks (GNNs) and Probabilistic Graphical
Models (PGMs), have demonstrated their exceptional capabilities across numerous fields …

Among the various driving situations, there are challenging road conditions where both the
texture and curvature are variables over time (eg, mountainous area). However, it is found …

In recent years, analyzing the explanation for the prediction of Graph Neural Networks
(GNNs) has attracted increasing attention. Despite this progress, most existing methods do …

We propose a new approach for propagating stable probability distributions through neural
networks. Our method is based on local linearization, which we show to be an optimal …

This PhD thesis thoroughly examines the utilization of deep learning techniques as a means
to advance the algorithms employed in the monitoring and optimization of electric power …