Music style translation has recently gained attention among music processing studies. It aims to generate variations of existing music pieces by altering the style-variant characteristics of the original music piece, while ...

In Natural Language Processing (NLP), neural-based knowledge transfer, which is to transfer out-of-domain (OOD) knowledge to task-specific neural networks, has been applied to many NLP tasks. To further explore neural-based ...

Learning a suitable graph is an important precursor to many graph signal processing (GSP) tasks, such as graph signal compression and denoising. Previous graph learning algorithms either make assumptions on graph connectivity ...

This thesis investigates a first-order extension of GL called ML3. We briefly discuss the latters properties and some of its toolbox: some metatheorems, the conservation theorem, and its semantic completeness (with respect ...

Several fairness definitions have been proposed in the machine learning literature to rectify the issue of demographic groups being treated differently. Given the substantial research in the field, this work aims to provide ...

The lack of transparent output behavior is a significant source of mistrust in many of the currently most successful machine learning tools. Concern arises particularly in situations where the data generation changes, for ...

The role of detecting affects from text is to detect affective states such as mood, sentiment and emotions from textual data. The main affective tasks, including sentiment analysis, emotion classification and sarcasm ...

The Question Answering (QA) task aims at building systems that can automatically answer a question or query about the given document(s). In this thesis, we utilize the transformer, a state-of-the-art neural architecture ...