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...

In machine learning, deep learning focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation...

synapses. In machine learning, an artificial neural network is a mathematical model used to approximate nonlinear functions. Artificial neural networks are used...

accelerate artificial intelligence (AI) and machine learning applications, including artificial neural networks and computer vision. Their purpose is either...

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...

include hyperparameter optimization, meta-learning and neural architecture search. In a typical machine learning application, practitioners have a set of...

Closely related are artificial neural networks, machine learning models inspired by biological neural networks. They consist of artificial neurons, which are...

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

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

incremental learning. Examples of incremental algorithms include decision trees (IDE4, ID5R and gaenari), decision rules, artificial neural networks (RBF networks...

In machine learning, a neural field (also known as implicit neural representation, neural implicit, or coordinate-based neural network), is a mathematical...

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

learning, and autoencoders. After the rise of deep learning, most large-scale unsupervised learning have been done by training general-purpose neural...

conventional machine learning models used for these applications. The prior knowledge of general physical laws acts in the training of neural networks (NNs)...

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...

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

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

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

similarities between certain physical systems and learning systems, in particular neural networks. For example, some mathematical and numerical techniques...

Bozinovski and Fulgosi published a paper addressing transfer learning in neural network training. The paper gives a mathematical and geometrical model...

instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical algorithms...

Neural network (machine learning), a network of mathematical neurons used in computation Neural network or Neural Networks may also refer to: Neural Networks...

Google Neural Machine Translation (GNMT) was a neural machine translation (NMT) system developed by Google and introduced in November 2016 that used an...

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

The Switching Neural Network approach was developed in the 1990s to overcome the drawbacks of the most commonly used machine learning methods. In particular...

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

(2014). "Neural Machine Translation by Jointly Learning to Align and Translate". arXiv:1409.0473 [cs.CL]. Wang, Qian (2014). Attentional Neural Network: Feature...

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

Examples include supervised neural networks, multilayer perceptrons, and dictionary learning. In unsupervised feature learning, features are learned with...