In machine learning, backpropagation is a gradient estimation method commonly used for training neural networks to compute the network parameter updates...

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

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

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

is not linearly separable. Modern neural networks are trained using backpropagation and are colloquially referred to as "vanilla" networks. MLPs grew out...

bi-directional flow). Modern feedforward networks are trained using backpropagation, and are colloquially referred to as "vanilla" neural networks. The...

training neural networks with gradient-based learning methods and backpropagation. In such methods, during each training iteration, each neural network...

mathematician and computer scientist known for creating the modern version of backpropagation. He was born in Pori. He received his MSc in 1970 and introduced a...

co-author of a highly cited paper published in 1986 that popularised the backpropagation algorithm for training multi-layer neural networks, although they were...

actual target values in a given dataset. Gradient-based methods such as backpropagation are usually used to estimate the parameters of the network. During...

introduced by Kunihiko Fukushima in 1979, though not trained by backpropagation. Backpropagation is an efficient application of the chain rule derived by Gottfried...

Rprop, short for resilient backpropagation, is a learning heuristic for supervised learning in feedforward artificial neural networks. This is a first-order...

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

"AI winter". Later, advances in hardware and the development of the backpropagation algorithm, as well as recurrent neural networks and convolutional neural...

Backpropagation training algorithms fall into three categories: steepest descent (with variable learning rate and momentum, resilient backpropagation);...

hand-designed. In 1989, Yann LeCun et al. at Bell Labs first applied the backpropagation algorithm to practical applications, and believed that the ability...

of backpropagation, such as the 1974 dissertation of Paul Werbos, as they did not know the earlier publications. Rumelhart developed backpropagation around...

Gradient descent SGD Quasi-Newton method Conjugate gradient method Backpropagation Attention Convolution Normalization Batchnorm Activation Softmax Sigmoid...

like the standard backpropagation network can generalize to unseen inputs, but they are sensitive to new information. Backpropagation models can be analogized...

biases are initialized, then iteratively updated during training via backpropagation. Zhang, Aston; Lipton, Zachary; Li, Mu; Smola, Alexander J. (2024)...

network uses memistors. As the sign function is non-differentiable, backpropagation cannot be used to train MADALINE networks. Hence, three different training...

each input during training. The most common training technique is the backpropagation algorithm. Neural networks learn to model complex relationships between...

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

"The influence of the sigmoid function parameters on the speed of backpropagation learning". In Mira, José; Sandoval, Francisco (eds.). From Natural...

such as backpropagation, might actually find such a sequence. Any method for searching the space of neural networks, including backpropagation, might find...

which vertices are visited is logged in a stack data structure. The backpropagation step then repeatedly pops off vertices, which are naturally sorted...

differentiable loss function in order to update the network weights through backpropagation. For variational autoencoders, the idea is to jointly optimize the...

Gradient descent SGD Quasi-Newton method Conjugate gradient method Backpropagation Attention Convolution Normalization Batchnorm Activation Softmax Sigmoid...

transformer. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights...

by modifying these weights through empirical risk minimization or backpropagation in order to fit some preexisting dataset. Neural networks are used...