Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …

Recent advancements in deep learning architecture have increased its utility in real-life
applications. Deep learning models require a large amount of data to train the model. In …

Financial time series analysis plays a central role in hedging market risks and optimizing
investment decisions. This is a challenging task as the problems are always accompanied …

Deep learning has revolutionized many machine learning tasks in recent years, ranging
from image classification and video processing to speech recognition and natural language …

Despite its great success, machine learning can have its limits when dealing with insufficient
training data. A potential solution is the additional integration of prior knowledge into the …

We introduce the framework of continuous--depth graph neural networks (GNNs). Graph
neural ordinary differential equations (GDEs) are formalized as the counterpart to GNNs …

The field of eXplainable Artificial Intelligence (XAI) focuses on providing explanations for AI
systems' decisions. XAI applications to AI-based Clinical Decision Support Systems (DSS) …

Objective To conduct a systematic review of deep learning models for electronic health
record (EHR) data, and illustrate various deep learning architectures for analyzing different …

Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

Multimodal deep learning systems that employ multiple modalities like text, image, audio,
video, etc., are showing better performance than individual modalities (ie, unimodal) …