Enter retrieval-augmented generation (RAG), a framework that’s here to keep AI’s feet on the ground and its head out of the clouds. RAG gives AI a lifeline to external, up-to-date sources of knowledge ...

Among the marvels of the human brain is its ability to generalize. We see an object, like a chair, and we know it's a chair, ...

Abstract: Heterogeneous Graph Neural Networks (HGNNs) have attracted significant research attention in recent years due to their ability to capture complex interactions among various node types in ...

A Fortran-based feed-forward neural network library. Whilst this library currently has a focus on 3D convolutional neural networks (CNNs), it can handle most standard hidden layer forms of neural ...

This repository contains the codebase and datasets for the LightGC²N project, which is associated with our research paper titled "Lightweight yet Fine-grained: A Graph Capsule Convolutional Network ...

Recurrent neural networks, the widely used framework in deep learning, suffer from the gradient vanishing and exploding problem, which limits their ability to learn long-term dependencies. To address ...

Deep neural networks process information in many layers, similarly to humans solving a puzzle step by step. The first layer, known as the input layer, brings in the raw data. The subsequent layers, ...

For the first time the Army plans to stress test its Next Generation Command and Control’s integrated data layer on classified networks at ... Skaggs said the process of making sense of data ...