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Scott's “How can Gradient Descent Connect with Goodhart?†looks remarkably relevant listed here. Most likely these results could serve as a partial solution to that issue, in the perception that SGD doesn't appear to vary very much from random search (that has a Gaussian prior within the weights) for deep neural networks on MNIST.
I do having said that disagree that People arguments could be applied to "pretty much any machine Discovering algorithm", Even though they unquestionably do implement to your much larger course of ML algorithms than just neural networks. However, I also Really don't Believe This can be always a nasty factor. The picture which the AIT arguments give makes it moderately unsurprising that you'd probably get the double-descent phenomenon while you improve the sizing of the model (at smaller sizes VC-dimensionality mechanisms dominate, but at larger sized sizes the overparameterisation starts to induce a simplicity bias, which inevitably starts to dominate).
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This is great! This undoubtedly does manage to me like solid proof that SGD is the incorrect area to search for knowing neural networks' inductive biases Which we need to be concentrating additional about the architecture alternatively.
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There's been some new investigation which (I think) sheds some gentle on this concern. I would like to call consideration to this web site submit: