A new model of learning centers on blasts of neural activity that act as teaching signals—approximating an algorithm called backpropagation.

A new technical paper titled “The backpropagation algorithm implemented on spiking neuromorphic hardware” was published by University of Zurich, ETH Zurich, Los Alamos National Laboratory, Royal ...

The Forward-Forward algorithm (FF) is comparable in speed to backpropagation but has the advantage that it can be used when the precise details of the forward computation are unknown.

Backpropagation, short for "backward propagation of errors," is an algorithm that lies at the heart of training neural networks.

Backpropagation has since become one of the most widely used algorithms in the field of artificial intelligence. After the publication of the backpropagation algorithm, it quickly became a popular ...

However, executing the widely used backpropagation training algorithm in multilayer neural networks requires information—and therefore storage—of the partial derivatives of the weight values ...

Obtaining the gradient of what's known as the loss function is an essential step to establish the backpropagation algorithm developed by University of Michigan researchers to train a material ...

Understanding Back-Propagation Back-propagation is arguably the single most important algorithm in machine learning. A complete understanding of back-propagation takes a lot of effort. But from a ...

Back Propagation is a common method of training artificial neural networks so as to minimize objective function. This paper describes the implementation of back propagation algorithm.

A new model of learning centers on bursts of neural activity that act as teaching signals — approximating backpropagation, the algorithm behind learning in AI.