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

learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological...

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

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

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

A feedforward neural network (FNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between...

Recurrent neural networks (RNNs) are a class of artificial neural network commonly used for sequential data processing. Unlike feedforward neural networks, which...

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

History of artificial neural networks History of knowledge representation and reasoning History of natural language processing Outline of artificial intelligence...

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...

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...

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

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

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

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

NETtalk is an artificial neural network. It is the result of research carried out in the mid-1980s by Terrence Sejnowski and Charles Rosenberg. The intent...

and his colleagues in June 2014. In a GAN, two neural networks contest with each other in the form of a zero-sum game, where one agent's gain is another...

The Open Neural Network Exchange (ONNX) [ˈɒnɪks] is an open-source artificial intelligence ecosystem of technology companies and research organizations...

analogy with biology (removal of components of an organism), and is particularly used in the analysis of artificial neural networks by analogy with ablative...

2016 that used an artificial neural network to increase fluency and accuracy in Google Translate. The neural network consisted of two main blocks, an...

Neural Networks is a monthly peer-reviewed scientific journal and an official journal of the International Neural Network Society, European Neural Network...

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

Improvements in transformer-based deep neural networks, particularly large language models (LLMs), enabled an AI boom of generative AI systems in the early...

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

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...

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

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

applications developed by OpenAI. In 2012, a University of Toronto research team used artificial neural networks and deep learning techniques to lower the error...

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

interdisciplinary meeting for researchers exploring biological and artificial Neural Networks. Reflecting this multidisciplinary approach, NeurIPS began in...