A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...

Claude isn't the most feature-rich AI chatbot, but it performs most tasks reasonably well and benefits from a well-designed ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

A new study of over 6,000 brain scans overturns the idea that global efficiency drives intelligence. Instead, researchers ...

One of the most striking outcomes of the surface-minimization framework is its ability to explain structural features that ...

This study investigated the dynamics of human cortical network activity with functional magnetic resonance imaging during movie watching and studied the modulation of these dynamics by subcortical ...

This is an important work implementing data mining methods on IMC data to discover spatial protein patterns related to the triple-negative breast cancer patients' chemotherapy response. The evidence ...

Abstract: Graph neural networks (GNNs) excel in graph representation learning by integrating graph structure and node features. Existing GNNs, unfortunately, fail to account for the uncertainty of ...

AI"s inability to continually learn remains one of the biggest problems standing in the way to truly general purpose models. Might it soon be solved?

The team’s research provides compelling evidence for the brain’s influence on physiology. A deeper understanding of this mind-body connection, they hope, could pave the way for new non-invasive ...

Knowledge representation is a fundamental aspect of AI, which allows machines to understand, think, and even make choices similarly to humans. By organizing inf ...

Abstract: Recently, topological graphs based on structural or functional connectivity of brain network have been utilized to construct graph neural networks (GNN) for Electroencephalogram (EEG) ...