Google researchers introduce ‘Internal RL,’ a technique that steers an models' hidden activations to solve long-horizon tasks ...

SPaDe-CSP first predicts most probable space groups and crystal densities using machine learning and then employs an efficient neural network potential for structure refinement. Prediction of crystal ...

Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

Abstract: Efficient workflow mapping and scheduling in heterogeneous HPC-Compute Continuum (HPC-CC) systems is critical for multi-objective optimization like optimizing resource utilization and ...

With the recent popularity of neural networks comes the need for efficient serving of inference workloads. A neural network inference workload can be represented as a computational graph with nodes as ...

The latest trends in software development from the Computer Weekly Application Developer Network. AI got to work. Well, to be clear, AI has “gotten” to work for us as it now permeates enterprise ...

Explore AI agents and workflows for YouTube automation. Learn AI content creation, marketing, and channel scaling with neural networks & ChatGPT. Trump tariffs found illegal by U.S. appeals court "SNL ...

Machine learning and neural nets can be pretty handy, and people continue to push the envelope of what they can do both in high end server farms as well as slower systems. At the extreme end of the ...

Abstract: Credit card fraud has been a persistent issue since the last century, causing significant financial losses to the industry. The most effective way to prevent fraud is by contacting customers ...