Nature

Three layered sparse dictionary learning algorithm for enhancing the subject wise segregation of brain networks

Independent component analysis (ICA) and dictionary learning (DL) are the most successful blind source separation (BSS) methods for functional magnetic resonance imaging (fMRI) data analysis. However, ...
Nature

Biological direct-shortcut deep residual learning for sparse visual processing

We show, based on the following three grounds, that the primary visual cortex (V1) is a biological direct-shortcut deep residual learning neural network (ResNet) for sparse visual processing: (1) We ...
EurekAlert!

Researchers present spontaneous sparse learning for PCM-based memristor neural networks

An international team of researchers, affiliated with UNIST has unveiled a novel technology that could improve the learning ability of artificial neural networks (ANNs). Professor Hongsik Jeong and ...
VentureBeat

DeepMind makes big jump toward interpreting LLMs with sparse autoencoders

Large language models (LLMs) have made remarkable progress in recent years. But understanding how they work remains a challenge and scientists at artificial intelligence labs are trying to peer into ...
Quanta Magazine

Sparse Networks Come to the Aid of Big Physics

Suppose you have a thousand-page book, but each page has only a single line of text. You’re supposed to extract the information contained in the book using a scanner, only this particular scanner ...
American Institute of Physics

Hybrid framework provides high-resolution reconstruction of hypersonic wall-pressure signals

AI to recover high-resolution hypersonic wall-pressure signals from sparse measurements. The framework combines a ...
Forbes

Sparse Models, The Math, And A New Theory For GroundBreaking AI

Video: This intriguing theory from a master of conceptual science might end up being crucial to new AI advances. Get ready for a lot of math…! We have sort of an intuitive understanding of a big need ...