Since the onset of the COVID-19 pandemic, there has been a growing interest in studying
epidemiological models. Traditional mechanistic models mathematically describe the …

Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …

Modeling customer shop** intentions is a crucial task for e-commerce, as it directly
impacts user experience and engagement. Thus, accurately understanding customer …

As training deep learning models on large dataset takes a lot of time and resources, it is
desired to construct a small synthetic dataset with which we can train deep learning models …

Heterogeneous feature spaces and technical noise hinder the cellular data integration and
imputation. The high cost of obtaining matched data across modalities further restricts …

Recent years have witnessed remarkable success achieved by graph neural networks
(GNNs) in many real-world applications such as recommendation and drug discovery …

Abstract Visual Question Answering (VQA) systems have achieved great success in general
scenarios. In medical domain, VQA systems are still in their infancy as the datasets are …

Cancer research encompasses data across various scales, modalities, and resolutions, from
screening and diagnostic imaging to digitized histopathology slides to various types of …

Single-cell RNA sequencing methods have led to improved understanding of the
heterogeneity and transcriptomic states present in complex biological systems. Recently, the …

Integrating single-cell multi-omics data is a challenging task that has led to new insights into
complex cellular systems. Various computational methods have been proposed to effectively …