Abstract: Heterogeneous graph neural networks (HGNNs) are effective for modeling multi-relational structured data. Existing HGNNs usually assume the training samples are relatively sufficient, thus ...

Abstract: Graph Neural Networks (GNNs) have emerged as a fundamental class of models for analyzing graph-structured data, with broad applications spanning social networks, computational neuroscience, ...

ABSTRACT: Background: The diagnosis and follow-up of mental disorders still rely heavily on subjective clinical assessments, highlighting the need for objective and quantitative monitoring methods.

Mesh TensorFlow (mtf) is a language for distributed deep learning, capable of specifying a broad class of distributed tensor computations. The purpose of Mesh TensorFlow is to formalize and implement ...