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

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

time series, where the order of elements is important. Unlike feedforward neural networks, which process inputs independently, RNNs utilize recurrent connections...

publication of ResNet made it widely popular for feedforward networks, appearing in neural networks that are seemingly unrelated to ResNet. The residual...

types of artificial neural network (ANN): feedforward neural network (FNN) or multilayer perceptron (MLP) and recurrent neural networks (RNN). RNNs have...

A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or signal pathways...

Highway Network was the first working very deep feedforward neural network with hundreds of layers, much deeper than previous neural networks. It uses...

statistics over 200 years ago. The simplest kind of feedforward neural network (FNN) is a linear network, which consists of a single layer of output nodes...

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

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

learning, a multilayer perceptron (MLP) is a name for a modern feedforward neural network consisting of fully connected neurons with nonlinear activation...

2016, decomposable attention applied a self-attention mechanism to feedforward networks, which are easy to parallelize, and achieved SOTA result in textual...

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...

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

multi-layer feedforward neural network return trained neural network Combining the ADAM algorithm and a multilayer feedforward neural network, we provide...

can use a variety of topologies and learning algorithms. In feedforward neural networks the information moves from the input to output directly in every...

management, neural networks, cognitive studies and behavioural science. Feed forward is a type of element or pathway within a control system. Feedforward control...

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

Instantaneously trained neural networks are feedforward artificial neural networks that create a new hidden neuron node for each novel training sample...

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

caused the field of neural network research to stagnate for many years, before it was recognised that a feedforward neural network with two or more layers...

represents a scene as a radiance field parametrized by a deep neural network (DNN). The network predicts a volume density and view-dependent emitted radiance...

A probabilistic neural network (PNN) is a feedforward neural network, which is widely used in classification and pattern recognition problems. In the PNN...

neural networks and denoising autoencoders are also under investigation. A deep feedforward neural network (DNN) is an artificial neural network with multiple...

A neural network, also called a neuronal network, is an interconnected population of neurons (typically containing multiple neural circuits). Biological...

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...

texts scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical...

with strong acceleration via graphics processing units (GPU) Deep neural networks built on a tape-based automatic differentiation system In 2001, Torch...

models trained from scratch. A Boltzmann machine is a type of stochastic neural network invented by Geoffrey Hinton and Terry Sejnowski in 1985. Boltzmann machines...

large-scale unsupervised learning have been done by training general-purpose neural network architectures by gradient descent, adapted to performing unsupervised...