In 540p-to-1080p comparisons, NSS improves stability and detail retention. It performs well in scenes with fast motion, ...

Researchers from Politecnico di Milano and international partners developed an in-situ training method for physical neural networks using light-based photonic chips, eliminating the need for digital ...

Researchers at Ruhr University Bochum developed a quartet-based deep learning method that embeds phylogenetic tree structures into neural networks. Tested on bacterial 16S rRNA data, it enables AI to ...

Understanding the brain's functional architecture is a fundamental challenge in neuroscience. The connections between neurons ultimately dictate how information is processed, transmitted, stored, and ...

Many people marvel at the speed of AI while also pondering: if they don't take the time to understand and learn about it, ...

This repository contains an experimental PyTorch implementation exploring the NoProp algorithm, presented in the paper "NOPROP: TRAINING NEURAL NETWORKS WITHOUT BACK-PROPAGATION OR FORWARD-PROPAGATION ...

In 2025, the integration of AI and Python will become increasingly tight. OpenAI's free inference model, o3 - mini, has excelled in areas such as mathematical code generation and physical simulation.

Abstract: As synthetic aperture radar (SAR) technology continues to evolve with a focus on miniaturization, reduced weight, and lower costs, its range of applications has broadened to encompass ...

This study presents valuable computational findings on the neural basis of learning new motor memories without interfering with previously learned behaviours using recurrent neural networks. The ...

Summary: Researchers have developed a new tool, bimodularity, that adds directionality to community detection in networks. Unlike traditional methods that only cluster nodes, this approach groups ...