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),...

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

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

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

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

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

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

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...

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

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

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

gradient descent are commonly used to train neural networks, through the backpropagation algorithm. Another type of local search is evolutionary computation...

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

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

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

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

activation within the network, the scale of gradient signals during backpropagation, and the quality of the final model. Proper initialization is necessary...

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

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

descent is the "backpropagation through time" (BPTT) algorithm, which is a special case of the general algorithm of backpropagation. A more computationally...

(NCCL). It is mainly used for allreduce, especially of gradients during backpropagation. It is asynchronously run on the CPU to avoid blocking kernels on the...

competitive learning rather than the error-correction learning (e.g., backpropagation with gradient descent) used by other artificial neural networks. The...

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

be contrasted with conventional deep learning techniques that use backpropagation (gradient descent on a neural network) with a fixed topology. Many...

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

Batch normalization (also known as batch norm) is a normalization technique used to make training of artificial neural networks faster and more stable...

precursor to variational autoencoders, which are instead trained using backpropagation. Helmholtz machines may also be used in applications requiring a supervised...

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

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