Phys.org

Using geometry and physics to explain feature learning in deep neural networks

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

In 1986, three scientists improved backpropagation, making neural networks learn effectively from data

A groundbreaking 1986 technique called backpropagation revolutionized artificial intelligence, enabling computers to learn ...
Science Daily

How do neural networks learn? A mathematical formula explains how they detect relevant patterns

Neural networks have been powering breakthroughs in artificial intelligence, including the large language models that are now being used in a wide range of applications, from finance, to human ...
Nature

Explainability in Graph Neural Networks

Graph Neural Networks (GNNs) have emerged as a powerful class of models for learning from graph-structured data, capturing complex relational patterns across nodes and edges. However, their inherent ...