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

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...

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

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

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

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

equal to the input dimension, then a deep neural network is not a universal approximator. The probabilistic interpretation derives from the field of machine...

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

A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a...

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

machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers...

generate this mapping include neural networks, dimensionality reduction on the word co-occurrence matrix, probabilistic models, explainable knowledge...

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

energy-efficient implementation of random neural networks was demonstrated by Krishna Palem et al. using the Probabilistic CMOS or PCMOS technology and was shown...

Morin, Frederic; Bengio, Yoshua (2005-01-06). "Hierarchical Probabilistic Neural Network Language Model" (PDF). International Workshop on Artificial Intelligence...

Neural Network (BCPNN) is an artificial neural network inspired by Bayes' theorem, which regards neural computation and processing as probabilistic inference...

function network (RBF) General regression neural network (GRNN) Probabilistic neural network (PNN) Self-organizing map (SOM) Time-lag recurrent network (TLRN)...

Parliamentary and News Network, Australia Princeton Municipal Airport (Maine), USA (by IATA code) Probabilistic neural network, in machine learning Pinin...

machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine...

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

Berckmans, D. (2001). "Recognition System for Pig Cough based on Probabilistic Neural Networks". Journal of Agricultural Engineering Research. 79 (4): 449–457...

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...

fields. They are a type of neural network whose parameters and predictions are both probabilistic. While standard neural networks often assign high confidence...

A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional...

learning, a multilayer perceptron (MLP) is a name for a modern feedforward neural network consisting of fully connected neurons with nonlinear activation functions...

Introduced by Radford Neal in 1992, this network applies ideas from probabilistic graphical models to neural networks. A key difference is that nodes in graphical...

an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. It is part of the families of probabilistic graphical models...

computer science, a convolutional deep belief network (CDBN) is a type of deep artificial neural network composed of multiple layers of convolutional restricted...

with strong acceleration via graphics processing units (GPU) Deep neural networks built on a tape-based automatic differentiation system In 2001, Torch...