Pre-training (PT) has been used successfully in many areas of machine learning. One area
where PT would be extremely impactful is over electronic health record (EHR) data …

The psychological costs of the attention economy are often considered through the binary of
harmful design and healthy use, with digital well-being chiefly characterised as a matter of …

Aphasia is a common speech and language disorder, typically caused by a brain injury or a
stroke, that affects millions of people worldwide. Detecting and assessing Aphasia in …

Sepsis is a deadly condition affecting many patients in the hospital. There have been many
efforts to build models that predict the onset of sepsis, but these models tend to perform …

Designing deep neural network classifiers that perform robustly on distributions differing
from the available training data is an active area of machine learning research. However, out …

Aphasia is a language disorder that affects the speaking ability of millions of patients. This
paper presents a new benchmark for Aphasia speech recognition and detection tasks using …

Health-related speech datasets are often small and varied in focus. This makes it difficult to
leverage them to effectively support healthcare goals. Robust transfer of linguistic features …

Optimal transport has been very successful for various machine learning tasks; however, it is
known to suffer from the curse of dimensionality. Hence, dimensionality reduction is …

To train a classification model that is robust to distribution shifts upon deployment, auxiliary
labels indicating the various “environments” of data collection can be leveraged to mitigate …

Automatic analysis of aphasic speech based on speech technology has been extensively
investigated in recent years, but there has been a few studies on Chinese languages. In this …