What’s the secret to prompting an AI to solve math problems that have left humans stumped? Tell it to believe in itself ...

New from Evertrace reveals 112 DeepMind alumni have launched startups since Q2 2025. Explore how the “DeepMind Mafia” is fueling a $5B+ tech boom across the UK and Europe. Former Google DeepMind ...

The Univalence Principle is the informal statement that equivalent mathematical structures are indistinguishable. There are various ways of making this statement formally precise, and a long history ...

This work extends the current framework of Geometric Deep Learning to incorporate local symmetries, specifically fibration symmetries, which are more commonly found in real-world data. By introducing ...

Multi-View Conditional Information Bottleneck (MVCIB) is a novel architecture for pre-training Graph Neural Networks on 2D and 3D molecular structures and developed by NS Lab, CUK based on pure ...

To effectively evaluate a system that performs operations on UML class diagrams, it is essential to cover a large variety of different types of diagrams. The coverage of the diagram space can be ...

Abstract: Existing deep learning-based circuit design methods mostly focused on the primary matching of the model itself or circuit data, lacking generalizability and ignoring deep representation of ...

Control flow graphs (CFGs) and function call graphs (FCGs) have become pivotal in providing a detailed understanding of program execution and effectively characterizing the behaviour of malware. These ...

Over the past few years, graph neural networks and graph transformers have been successfully used to analyze graph-structured data, mainly focusing on node classification and link prediction tasks.