Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Federated learning (FL), which provides a collaborative training scheme for distributed data
sources with privacy concerns, has become a burgeoning and attractive research area. Most …

The recent explosion of interest in multimodal applications has resulted in a wide selection
of datasets and methods for representing and integrating information from different …

In healthcare, machine learning (ML) shows significant potential to augment patient care,
improve population health, and streamline healthcare workflows. Realizing its full potential …

Affective computing has an unprecedented potential to change the way humans interact with
technology. While the last decades have witnessed vast progress in the field, multimodal …

Many real-world problems are inherently multimodal, from spoken language, gestures, and
paralinguistics humans use to communicate, to force, proprioception, and visual sensors on …

Biomedical data is inherently multimodal, consisting of electronic health records, medical
imaging, digital pathology, genome sequencing, wearable sensors, and more. The …

Personalized prediction is a machine learning approach that predicts a person's future
observations based on their past labeled observations and is typically used for sequential …

Major depressive disorder (MDD) is a prevalent mental health condition and has become a
pressing societal challenge. Early prediction of treatment response may aid in the …

Machine learning (ML) in healthcare presents numerous opportunities for enhancing patient
care, population health, and healthcare providers' workflows. However, the real-world …