Figure introduced Helix 2.5 on September 17, 2026, a humanoid neural network pretrained on the company's Index dataset of human behavior and evaluated across 30 Bay Area homes with no data collected ...

This workshop introduces Graph Neural Networks (GNNs) for geospatial practitioners. Using open-source Python tools including PyTorch Geometric and City2Graph, participants will learn how to transform ...

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 ...

In this tutorial, we will generate knowledge graphs from plain text, conversations, and multiple source documents using kg-gen. We start by setting up the required dependencies and configuring an LLM ...

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If neural networks are now making decisions everywhere from code editors to safety systems, how can we actually see the specific circuits inside that drive each behavior? OpenAI has introduced a new ...

Human brain–inspired neural-network computing hardware has the potential to overcome the energy efficiency and resource overhead challenges caused by the von Neumann bottleneck. However, existing ...

Transition metal complexes (TMCs) are of great scientific and practical interest for applications in catalysis, biological systems, photochemistry, and sustainability, with properties highly dependent ...

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 ...

Through AI frameworks and libraries, businesses can build and craft their AI solutions to realise efficiencies and optimisations that yield real returns Software plays a crucial role in streamlining ...