Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...

Abstract: Graph neural networks (GNNs) are specifically designed for graph-structured data and have gained significant attention. However, training GNN on large-scale graphs remains challenging due to ...

The U.S. Space Force has released Vector 2025, a consolidated reference outlining the direction and momentum the service intends to maintain as it continues its transition into a warfighting ...

When I first wrote “Vector databases: Shiny object syndrome and the case of a missing unicorn” in March 2024, the industry was awash in hype. Vector databases were positioned as the next big thing — a ...

New AI Graph Toolkit makes it 10x easier to port SQL and unstructured data into a knowledge graph Developers accelerate access to GraphRAG to meet business users’ demands for accurate ChatGPT-style ...

Vector databases (DBs), once specialist research instruments, have become widely used infrastructure in just a few years. They power today's semantic search, recommendation engines, anti-fraud ...

This is a snippet from the VECTOR_GRAPH_SEARCH_QUERY from data source context retrieval. From I am able to gather, here is where we take not just the entities, but also the relations between them.

For a long time, companies have been using relational databases (DB) to manage data. However, with the increasing use of large AI models, integration with graph databases is now required. This process ...

Introduction: Predicting interactions between microRNAs (miRNAs) and competing endogenous RNAs (ceRNAs), including long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), is essential for ...