Trimble’s SaaS transition and strategic restructuring aim for margin growth and stable cash flows. Click here to read an ...

Graph theory analysis, a mathematical approach, has been applied in brain connectivity studies to explore the organization of network patterns. The computation of graph theory metrics enables the ...

The model also proved scalable, maintaining an inference time of 2.5 ms per sample on graphs of up to 10,000 nodes, making it viable for real-time deployment. These findings confirm that graph-based ...

A deep generative model based on a variational autoencoder (VAE), conditioned simultaneously by two target properties, is developed to inverse design stable magnetic materials. The structure of the ...

In recent years, with the public availability of AI tools, more people have become aware of how closely the inner workings of ...

This review examines AI and ML's role in transforming thermoelectric materials design, focusing on defect engineering and ...

Forecasting is a fundamentally new capability that is missing from the current purview of generative AI. Here's how Kumo is changing that.

A new machine learning method is presented for extracting interpretable structure−activity relationships from screening data. The method is based on an evolutionary algorithm and reduced graphs and ...

Learn how Pigment’s AI-driven platform transforms planning, forecasting, budgeting, and decision-making with cutting-edge ...

BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs ...

Chain-of-Thought (CoT) Distillation. When faced with diverse graph data, language models may encounter new or unfamiliar patterns and structures. This distribution shift can pose challenges in ...