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.

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 ...

In early December, dozens of alternatives to traditional backpropagation were proposed during a workshop at the NeurIPS 2020 conference, which took place virtually. Some leveraged hardware like ...

For example, the calculus derivative of the hyperbolic tangent function is (1 - y) (1 + y). Because the back-propagation algorithm requires the derivative, only functions that have derivatives can be ...

Here, we propose a hardware implementation of the backpropagation algorithm that progressively updates each layer using in situ stochastic gradient descent, avoiding this storage requirement.

For example, the calculus derivative of the hyperbolic tangent function is (1 - y) (1 + y). Because the back-propagation algorithm requires the derivative, only functions that have derivatives can be ...