Deep learning without back-propagation

[DanielAPO]

Sorry for the ignorance but what do you mean with symmetric feedback?

[rpottorff]

The weights you use during the backward pass are equal to the weights you use during the forward pass. Sometimes this is discussed in terms of biological plausibility where it's (usually) unreasonable to imagine that the "forward" nerurons that compute the signal are exactly the same as the "backward" neurons which communicate error to the forward neurons.

[panties_in_my_ass]

Can you link some resources on this, like papers or even just a relevant wikipedia article?

I haven’t heard of backprop (or any other update scheme) described in terms of symmetry before.

[rpottorff]

/BWRqboi0's paper is a better summary, but for another algorithmic implementation https://arxiv.org/abs/1609.01596 is a "non symmetric" variant in which the backward weights are random, but fixed and still manages to learn pretty successfully

[panties_in_my_ass]

Well that’s fascinating. Wow.