In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure...

Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. Their creation was inspired by biological neural...

an artificial neural network is a mathematical model used to approximate nonlinear functions. Artificial neural networks are used to solve artificial intelligence...

many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used...

from an existing artificial neural network. The goal of this process is to reduce the size (parameter count) of the neural network (and therefore the...

function. However, typical research in quantum neural networks involves combining classical artificial neural network models (which are widely used in machine...

Feedforward refers to recognition-inference architecture of neural networks. Artificial neural network architectures are based on inputs multiplied by weights...

Biological neural networks are studied to understand the organization and functioning of nervous systems. Closely related are artificial neural networks, machine...

two types of artificial neural network (ANN): feedforward neural network (FNN) or multilayer perceptron (MLP) and recurrent neural networks (RNN). RNNs...

An artificial neuron is a mathematical function conceived as a model of a biological neuron in a neural network. The artificial neuron is the elementary...

Fast Artificial Neural Network (FANN) is cross-platform programming library for developing multilayer feedforward artificial neural networks (ANNs). It...

Spiking neural networks (SNNs) are artificial neural networks (ANN) that mimic natural neural networks. These models leverage timing of discrete spikes...

In the context of artificial neural networks, the rectifier or ReLU (rectified linear unit) activation function is an activation function defined as the...

In artificial neural networks, recurrent neural networks (RNNs) are designed for processing sequential data, such as text, speech, and time series, where...

physical neural network is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse...

Graph neural networks (GNN) are specialized artificial neural networks that are designed for tasks whose inputs are graphs. One prominent example is molecular...

NETtalk is an artificial neural network that learns to pronounce written English text by supervised learning. It takes English text as input, and produces...

An optical neural network is a physical implementation of an artificial neural network with optical components. Early optical neural networks used a photorefractive...

A Siamese neural network (sometimes called a twin neural network) is an artificial neural network that uses the same weights while working in tandem on...

A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep...

An artificial neural network (ANN) or neural network combines biological principles with advanced statistics to solve problems in domains such as pattern...

A residual neural network (also referred to as a residual network or ResNet) is a deep learning architecture in which the layers learn residual functions...

A capsule neural network (CapsNet) is a machine learning system that is a type of artificial neural network (ANN) that can be used to better model hierarchical...

neural networks called "backpropagation". These two developments helped to revive the exploration of artificial neural networks. Neural networks, along...

system designed to accelerate artificial intelligence (AI) and machine learning applications, including artificial neural networks and computer vision. Their...

or video and replace them with someone else's likeness using artificial neural networks. Deepfakes have garnered widespread attention and concerns for...

another to form large scale brain networks. Neural circuits have inspired the design of artificial neural networks, though there are significant differences...

Neural network software is used to simulate, research, develop, and apply artificial neural networks, software concepts adapted from biological neural...

A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce...

artificial neural networks by preventing complex co-adaptations on training data. They are an efficient way of performing model averaging with neural...