The random neural network (RNN) is a mathematical representation of an interconnected network of neurons or cells which exchange spiking signals. It was...

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

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...

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

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

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

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

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

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...

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

In artificial intelligence, a differentiable neural computer (DNC) is a memory augmented neural network architecture (MANN), which is typically (but not...

A Neural Network Gaussian Process (NNGP) is a Gaussian process (GP) obtained as the limit of a certain type of sequence of neural networks. Specifically...

parameter initialization describes the initial step in creating a neural network. A neural network contains trainable parameters that are modified during training:...

width limit of Bayesian neural networks, and to the distribution over functions realized by non-Bayesian neural networks after random initialization. The...

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...

A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce...

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

subset of machine learning that focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation...

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...

artificial neural networks (ANNs), the neural tangent kernel (NTK) is a kernel that describes the evolution of deep artificial neural networks during their...

artificial neural network to abruptly and drastically forget previously learned information upon learning new information. Neural networks are an important...

Recurrent Random Neural Network". Neural Computation. 5 (1): 154–164. doi:10.1162/neco.1993.5.1.154. E. Gelenbe "Product-Form queueing networks with negative...

multiplicative units. Neural networks using multiplicative units were later called sigma-pi networks or higher-order networks. LSTM became the standard...

spiking behaviour, he introduced the precursor of G-Networks, calling it the random neural network. By introducing "negative customers" which can destroy...

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

Transportation and mobility networks, Internet, mobile phone networks, power grids, social and contact networks and biological neural networks are all examples where...

"Orthogonal Random Features". Advances in Neural Information Processing Systems. 29. Curran Associates, Inc. Recht, Benjamin. "Reflections on Random Kitchen...

An echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer (with typically...

Random neural network, a mathematical representation of an interconnected network of neurons or cells which exchange spiking signals Recurrent neural...

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