In machine learning, backpropagation is a gradient computation method commonly used for training a neural network in computing parameter updates. It is...

Neural backpropagation is the phenomenon in which, after the action potential of a neuron creates a voltage spike down the axon (normal propagation), another...

multiplication remains the core, essential for backpropagation or backpropagation through time. Thus neural networks cannot contain feedback like negative...

to Neural Networks (D. Kriesel) – Illustrated, bilingual manuscript about artificial neural networks; Topics so far: Perceptrons, Backpropagation, Radial...

Backpropagation through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks, such as Elman networks. The...

Ostrovski et al. republished it in 1971. Paul Werbos applied backpropagation to neural networks in 1982 (his 1974 PhD thesis, reprinted in a 1994 book...

Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by the regularization that comes from...

distinguish data that is not linearly separable. Modern neural networks are trained using backpropagation and are colloquially referred to as "vanilla" networks...

the vanishing gradient problem of automatic differentiation or backpropagation in neural networks in 1992. In 1993, such a system solved a "Very Deep Learning"...

hardware and the development of the backpropagation algorithm, as well as recurrent neural networks and convolutional neural networks, renewed interest in ANNs...

paper published in 1986 that popularised the backpropagation algorithm for training multi-layer neural networks, although they were not the first to...

Romania: IEEE. Werbos, Paul J. (1994). The Roots of Backpropagation. From Ordered Derivatives to Neural Networks and Political Forecasting. New York, NY:...

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...

Backpropagation through structure (BPTS) is a gradient-based technique for training recursive neural networks, proposed in a 1996 paper written by Christoph...

action potential back towards the cell body. In some cells, however, neural backpropagation does occur through the dendritic branching and may have important...

empirical risk minimization or backpropagation in order to fit some preexisting dataset. The term deep neural network refers to neural networks that have more...

(2017). "The Reversible Residual Network: Backpropagation Without Storing Activations". Advances in Neural Information Processing Systems. 30. Curran...

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

plasticity Brain training Environmental enrichment (neural) Neural adaptation Neural backpropagation Neuronal sprouting Neuroplastic effects of pollution...

catastrophic interference during two different experiments with backpropagation neural network modelling. Experiment 1: Learning the ones and twos addition...

and later layers encountered when training neural networks with backpropagation. In such methods, neural network weights are updated proportional to...

weights. Neural networks also started to be used as a general function approximation model. The best known training algorithm called backpropagation has been...

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

process of training artificial neural networks through backpropagation of errors. He also was a pioneer of recurrent neural networks. Werbos was one of the...

Almeida–Pineda recurrent backpropagation is an extension to the backpropagation algorithm that is applicable to recurrent neural networks. It is a type...

function space U {\displaystyle {\mathcal {U}}} . Neural operators can be trained directly using backpropagation and gradient descent-based methods. Another...

gradient descent-based backpropagation (BP) is not available. SNNs have much larger computational costs for simulating realistic neural models than traditional...

consequence, the chances of field summation are slim. However, neural backpropagation, as a typically longer dendritic current dipole, can be picked up...

History of Neural Networks. Reprint edition. The MIT Press. Rumelhart, D. E.; Durbin, R.; Golden, R.; Chauvin, Y. (1995). "Chapter 1. Backpropagation: The basic...

gradient is computed using backpropagation through structure (BPTS), a variant of backpropagation through time used for recurrent neural networks. The universal...