Backpropagation is a boosting algorithm. See page 9 of:https://arxiv.org/abs/2012.00152
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Yeah, but that's like saying: "every function is linear when the input is represented in a space of high-enough dimension", or: "every dynamical system can be approximated by a Markov chain." It's both true and considerably less relevant in practice than one might think.
It's relevant when it leads to better algorithms, as both of those have (kernel machines and sequential Monte Carlo methods, respectively).
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We could use more effective learning, but what I don't think deep learning naysayers get is that DL is above threshold. That's why there will be no AI winter. For the first time, we have something good enough with glue/protection whatever to be of easy and general use.
Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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