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

many of them together in a network can perform complex tasks. There are two main types of neural networks. In neuroscience, a biological neural network is...

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

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

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

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...

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

neural networks, machine learning models inspired by biological neural networks. They consist of artificial neurons, which are mathematical functions that...

optimization, meta-learning and neural architecture search. In a typical machine learning application, practitioners have a set of input data points to...

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

in humans, the attention mechanism was developed to address the weaknesses of using information from the hidden layers of recurrent neural networks....

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

Techniques of Algorithmic Differentiation (Second ed.). SIAM. ISBN 978-0898716597. Schmidhuber, Jürgen (2015). "Deep learning in neural networks: An overview"...

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

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...

optimization. In 1976, Bozinovski and Fulgosi published a paper addressing transfer learning in neural network training. The paper gives a mathematical and geometrical...

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...

which result in high label prediction accuracy. Examples include supervised neural networks, multilayer perceptrons, and dictionary learning. In unsupervised...

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

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

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

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

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

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...

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

learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), excel in processing data on regular grids and sequences...

hand, is specific to deep learning, and includes methods that rescale the activation of hidden neurons inside neural networks. Normalization is often used...

particular neural networks. For example, some mathematical and numerical techniques from quantum physics are applicable to classical deep learning and vice versa...

Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a...