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...

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...

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

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 mathematical...

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

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

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...

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...

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

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

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

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

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...

That is, the family of neural networks is dense in the function space. The most popular version states that feedforward networks with non-polynomial activation...

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...

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

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

defined by the two frameworks were mutually incompatible. The Open Neural Network Exchange (ONNX) project was created by Meta and Microsoft in September...

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...

there are feedforward networks f 1 , . . . , f n {\displaystyle f_{1},...,f_{n}} , and a gating network w {\displaystyle w} . The gating network is defined...

"polynomial feedforward neural network", or "self-organization of models". It was one of the first deep learning methods, used to train an eight-layer neural net...

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