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>Bayesian can be seen as a subset of deep learning or hell a superset.

eh-hem

DIE, HERETIC!

eh-hem

Ok, with that out of my system, no, Bayesian methods are definitely not a subset of deep learning, in any way. Hierarchical Bayes could be labeled "deep Bayesian methods" if we're marketing jerks, but Bayesian methods mostly do not involve neural networks with >3 hidden layers. It's just a different paradigm of statistics.



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My mentor was very very adamant about Bayesian network and hierarchical as being deep learning.

He sees the latent layer in the hierarchical model as the hidden layer and the Bayesian just have a strict restrictions/assumptions to the network where as the deep learning is more dumb and less assuming. A few of my professor thinks that PGM, probability graphical model is a super set of deep learning/neural network.

This is where my thinking come from.

IIRC, a paper have shown that gradient descent seems to exhibit MCMCs (blog with paper link inside that led to this conclusion of mine: http://www.inference.vc/everything-that-works-works-because-...).

But I am not an expert in Neural Network nor know the topic well enough to say such a thing. Other than was deferring to opinions of some one that's better than myself. So I'll keep this in mind and hopefully one day have the time to do more research into this topic.

Thank you.


I think your link, and your mentor, are somewhat fundamentalist about their Bayesianism.

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