How does it do on CIFAR-10, or even better, ImageNet?
Interesting research, not sure it's a backprop alternative.
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EDIT: accuracy on MNIST is not ~90%. It's ~85%.
qarl 24 minutes ago [-]
They state replacing backprop is not their goal.
Their goal is to understand how distributed systems which cannot do backprop (the brain) can still do learning.
Lerc 2 hours ago [-]
It might be beneficial while not being optimal on its own.
The obvious example is if it has different behaviour around local minima, it could be an altenate pathway out.
I have often wondered if doing training with radically different aproaches for the first few iterarions would avoid any method specific artifacts before the weights had time to denoise.
How does it do on CIFAR-10, or even better, ImageNet?
Interesting research, not sure it's a backprop alternative.
===
EDIT: accuracy on MNIST is not ~90%. It's ~85%.
Their goal is to understand how distributed systems which cannot do backprop (the brain) can still do learning.
The obvious example is if it has different behaviour around local minima, it could be an altenate pathway out.
I have often wondered if doing training with radically different aproaches for the first few iterarions would avoid any method specific artifacts before the weights had time to denoise.
I didn't see ImageNet. TinyImageNet is something else.
[1]: https://arxiv.org/abs/2605.31022