With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …

With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

Understanding which brain regions are related to a specific neurological disorder or
cognitive stimuli has been an important area of neuroimaging research. We propose …

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

Functional connectivity (FC) between regions of the brain can be assessed by the degree of
temporal correlation measured with functional neuroimaging modalities. Based on the fact …

Deep learning analyses have offered sensitivity leaps in detection of cognition-related
variables from functional MRI (fMRI) measurements of brain responses. Yet, as deep models …

Graph neural networks (GNNs) have received increasing interest in the medical imaging
field given their powerful graph embedding ability to characterize the non-Euclidean …

Convolutional neural networks (CNN) have been frequently used to extract subject-invariant
features from electroencephalogram (EEG) for classification tasks. This approach holds the …

Abstract Machine learning provides a valuable tool for analyzing high-dimensional
functional neuroimaging data, and is proving effective in predicting various neurological …

A task-oriented dialogue system (TOD) is an important application of artificial intelligence. In
the past few years, works on multi-domain TODs have attracted increased research attention …