Born in the 1950s, the concept of an artificial neural network has progressed considerably. Today, known as “deep learning”, its uses have expanded to many areas, including finance.

This collection welcomes submissions on explainability techniques for deep learning neural networks, encompassing diverse neural architectures and ensuring broad applicability to different domains.

In the realm of artificial intelligence and machine learning, the Multilayer Perceptron (MLP) stands tall as a fundamental class of feedforward artificial neural networks.

The deep learning pioneers believe that better neural network architectures will eventually lead to all aspects of human and animal intelligence, including symbol manipulation, reasoning, causal ...

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 ...

A new idea called the “information bottleneck” is helping to explain the puzzling success of today’s artificial-intelligence algorithms—and might also explain how human brains learn.

AI transforms RF engineering through neural networks that predict signal behavior and interference patterns, enabling ...