Researchers are enhancing graph node classification by combining graph neural networks with large language models to identify both familiar and entirely new types of data points.
A new framework uses interconnected knowledge to significantly improve how large language models find and reason with relevant information.
Researchers have developed a novel model to identify and categorize hateful content and misinformation, even when it’s expressed in mixed languages.
Researchers have developed a generative model that leverages graph neural networks to automatically create functional and realistic architectural layouts.
A new pipeline harnesses the power of adaptable AI models to automatically extract crucial details from police announcements shared on social media platforms.
A new approach uses reinforcement learning to dynamically adjust the complexity of attention mechanisms within large language models, reducing computational cost without sacrificing performance.
A new analysis technique leverages the power of machine learning to unlock the chemical composition of millions of stars, offering unprecedented insights into the origins of elements in the cosmos.
Researchers have designed a novel automated market maker protocol that brings the flexibility of decentralized finance to traditionally illiquid fixed-income assets.
A new method for analyzing passenger flow in transportation networks reveals how understanding origin, destination, and time can optimize services and resource allocation.
A new framework proposes treating data categories as a portfolio to improve accountability, mitigate risk, and enable transparent data allocation for artificial intelligence systems.