Stacked Approximated Regression Machine: A Simple Deep Learning Approach

Re:Stacked Approximated Regression Machine: A Simple Deep Le... by joe_the_user on Monday September 05, 2016 @09:31PM (#12433160)

The paper itself is fairly sparse but references a number of approaches for more-quickly-learning neural-net-related learning systems from ~2013 (PCAnet, SCATnet,etc).

The paper presents these and other approaches as being instances of a classical, general form, regularized regression but with the "stacked" property involving each layer iterating however many times and then the next layer changing parameters (or features) and further iterating.

From my barely-informed viewpoint, this sounds like a fascinating way to unify the earlier efforts and one which could yield a variety of other approaches - even if the particular variation they use doesn't work out. But I assume lots of more-informed people are going to be looking at this.

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Re:Stacked Approximated Regression Machine: A Simple Deep Le... by joe_the_user on Friday September 09, 2016 @07:05PM (#12466659)

Note: even though the paper talks about Approximate Regression Machine layers and uses equations that look sort-of like the equations of regularized regression, the layers aren't about regularized regression but about sparse dictionary encoding, a quite different approach.

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