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...

A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or signal pathways...

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

Recurrent neural networks (RNNs) are a class of artificial neural networks designed for processing sequential data, such as text, speech, and time series...

A neural network, also called a neuronal network, is an interconnected population of neurons (typically containing multiple neural circuits). Biological...

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

subset of machine learning that focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation...

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...

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

Physics-informed neural networks (PINNs), also referred to as Theory-Trained Neural Networks (TTNs), are a type of universal function approximators that...

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

Quantum neural networks are computational neural network models which are based on the principles of quantum mechanics. The first ideas on quantum neural computation...

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

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

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

developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks compete with each other in the form of a zero-sum game, where one agent's...

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...

Instantaneously trained neural networks are feedforward artificial neural networks that create a new hidden neuron node for each novel training sample...

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

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...

neural network in Wiktionary, the free dictionary. A neural network is an interconnected population of neurons: Neural network (biology), a network of...

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

learning, cellular neural networks (CNN) or cellular nonlinear networks (CNN) are a parallel computing paradigm similar to neural networks, with the difference...

of a biological neuron in a neural network. The artificial neuron is the elementary unit of an artificial neural network. The design of the artificial...

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...

A probabilistic neural network (PNN) is a feedforward neural network, which is widely used in classification and pattern recognition problems. In the PNN...

Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine...

A modular neural network is an artificial neural network characterized by a series of independent neural networks moderated by some intermediary. Each...

A Neural Network Gaussian Process (NNGP) is a Gaussian process (GP) obtained as the limit of a certain type of sequence of neural networks. Specifically...

Time delay neural network (TDNN) is a multilayer artificial neural network architecture whose purpose is to 1) classify patterns with shift-invariance...