Mental illness is highly prevalent nowadays, constituting a major cause of distress in
people's life with impact on society's health and well-being. Mental illness is a complex multi …

Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …

Collecting data is one of the bottlenecks of Human-Computer Interaction (HCI) research.
Motivated by this, we explore the potential of large language models (LLMs) in generating …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …

Learning medical visual representations directly from paired radiology reports has become
an emerging topic in representation learning. However, existing medical image-text joint …

Knowledge graph completion (KGC) aims to reason over known facts and infer the missing
links. Text-based methods such as KGBERT (Yao et al., 2019) learn entity representations …

The scarcity of high-quality annotated medical imaging datasets is a major problem that
collides with machine learning applications in the field of medical imaging analysis and …

We aim to understand grokking, a phenomenon where models generalize long after
overfitting their training set. We present both a microscopic analysis anchored by an effective …

Although BERT and its variants have reshaped the NLP landscape, it still remains unclear
how best to derive sentence embeddings from such pre-trained Transformers. In this work …