Geoffrey Hinton’s Forward-Forward Algorithm Charts a New Path for Neural Networks

2022-12-08 by Synced

there is increasing interest in whether the biological brain follows backpropagation or, as Hinton asks, whether it has some other way of getting the gradients needed to adjust the weights on its connections. In this regard, Hinton proposes the FF [Forward-Forward] algorithm as an alternative to backpropagation for neural network learning.

It aims to replace the forward and backward passes of backpropagation with two forward passes: a positive pass that operates on real data and adjusts weights “to improve the goodness in every hidden layer,” and a negative pass that operates on externally supplied or model-generated “negative data” and adjusts weights to deteriorate the goodness.

https://syncedreview.com/2022/12/08/geoffrey-hintons-forward-forward-algorithm-charts-a-new-path-for-neural-networks/

The Forward-Forward Algorithm: Some Preliminary
Investigations by Geoffrey Hinton
https://www.cs.toronto.edu/~hinton/FFA13.pdf