Artificial neural networks (ANN) are computational systems that "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.

Deep neural networks are a type of deep learning, which is a type of machine learning. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and ...

A research team has introduced a new out-of-core mechanism, Capsule, for large-scale GNN training, which can achieve up to a ...

Researchers create an iontronic artificial skin that senses pressure, temperature, and current while learning patterns ...

The diagram illustrates the interplay among data acquisition, machine learning, and experiment synthesis. Physical models such as thermodynamics and kinetics can be integrated into ML models as ...

It's obvious when a dog has been poorly trained. It doesn't respond properly to commands. It pushes boundaries and behaves unpredictably. The same is true with a poorly trained artificial intelligence ...

This document defines a unified architecture for Machine Learning in Fifth Generation and future networks. A comprehensive set of architectural requirements is presented, which leads to specific ...

Apr. 14, 2025 — A groundbreaking open-source computer program uses artificial intelligence to analyze videos of patients with Parkinson's disease and other movement disorders. The tool, called ...

Perceived similarity offers a window into the mental representations underlying our ability to make sense of our visual world, yet, the collection of similarity judgments quickly becomes infeasible ...

Network models are a computer architecture, implementable in either hardware or software, meant to simulate biological populations of interconnected neurons. These models, also known as ...

EDA software is revolutionizing high-speed digital design by accelerating time-to-market despite growing complexity.