A new graph neural network framework evaluates dance training in real time and automatically adapts practice plans, achieving ...

Introduction A few years ago, I was solely responsible for an in-house demand forecasting model. The scores on the validation ...

This is a library containing pyTorch code for creating graph neural network (GNN) models. The library provides some sample implementations. The library is mainly engineered to be fast for sparse ...

Got Tech, Data, AI and Media, and not afraid to use them. Are graphs really the new star schema? What do graphs look like to non-insiders, and what is it that attracts them to the graph community, ...

Emotion recognition based on electroencephalogram (EEG) signals has shown increasing application potential in fields such as brain-computer interfaces and affective computing. However, current graph ...

Digital twins, which replicate real-world entities through computational models, are transforming manufacturing and automation. While recent advances in machine learning have enabled data-driven ...

This document is for engineers and researchers (both individuals and teams) interested in maximizing the performance of deep learning models in AzureML. We assume basic knowledge of machine learning ...

AI success depends on whether enterprise data is ready, reachable, and close enough to the workloads that need it. In this eSpeaks episode, Dell Technologies’ Vrashank Jain explains why fragmented ...