Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?

Abstract: Graph-based deep learning models are becoming prevalent for data-driven traffic prediction in the past years, due to their competence in exploiting the non-euclidean spatial-temporal traffic ...

Abstract: In recent years, various deep learning architectures have been proposed to solve complex challenges (e.g. spatial dependency, temporal dependency) in traffic domain, which have achieved ...

has been cited by the following article: TITLE: Artificial Intelligence in Learning: An Integrative Framework for Education 4.0 ...

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DeepDrug is a deep learning framework, using residual graph convolutional networks (RGCNs) and convolutional networks (CNNs) to learn the comprehensive structural and sequential representations of ...

Recent advance in single-cell RNA sequencing (scRNA-seq) has enabled large-scale transcriptional characterization of thousands of cells in multiple complex tissues, in which accurate cell type ...

The objective of this research project is to develop general machine learning techniques for graph generation, with the end application of smart design including new material discovery, advanced ...

Dyania Health is using artificial intelligence to automate manual patient chart review, boosting providers' ability to comb through medical records and speeding up medical research. The female-founded ...

In the globalization trend, China's cultural heritage is in danger of gradually disappearing. The protection and inheritance of these precious cultural resources has become a critical task. This paper ...