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

structure and functions of biological neural networks. A neural network consists of connected units or nodes called artificial neurons, which loosely model...

functions. Artificial neural networks are used to solve artificial intelligence problems. In the context of biology, a neural network is a population of biological...

learning models inspired by biological neural networks. They consist of artificial neurons, which are mathematical functions that are designed to be analogous...

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

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

networks, deep belief networks, recurrent neural networks, convolutional neural networks, generative adversarial networks, transformers, and neural radiance...

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

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

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

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

training and convergence of deep neural networks with hundreds of layers, and is a common motif in deep neural networks, such as transformer models (e.g...

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

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

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

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

had in the past. Most of the new directions in AI relied heavily on mathematical models, including artificial neural networks, probabilistic reasoning...

integrated a wide range of techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics...

known as shift invariant or space invariant artificial neural networks, based on the shared-weight architecture of the convolution kernels or filters that...

brain networks. Neural circuits have inspired the design of artificial neural networks, though there are significant differences. Early treatments of neural...

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...

the mathematical theory of artificial neural networks, universal approximation theorems are theorems of the following form: Given a family of neural networks...

Generative adversarial networks (GANs) are an influential generative modeling technique. GANs consist of two neural networks—the generator and the...

University of Edinburgh. Each one developed its own style of research. Earlier approaches based on cybernetics or artificial neural networks were abandoned...

networks. The universal approximation capability of RNNs over trees has been proved in literature. Recurrent neural networks are recursive artificial...

International Joint Conference on Neural Networks with Bart Kosko in 1987. As a pioneer in the field of artificial neural networks, he authored the first textbook...

Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of...

work on artificial neural networks, which earned him the title "the Godfather of AI". Hinton is University Professor Emeritus at the University of Toronto...

separable. Modern neural networks are trained using backpropagation and are colloquially referred to as "vanilla" networks. MLPs grew out of an effort to improve...

top of other libraries). Microsoft Cognitive Toolkit (previously known as CNTK), an open source toolkit for building artificial neural networks. OpenNN...