The rate of new ideas in deep-learning-type subfields is starting to remind me of the Cambrian explosion, which generated in only a few million years a vast range of genetic novelty to be whittled down into the familiar stock of body plans we see today.<p>Indeed many of the interesting new papers are elaborations of old ideas along new themes, or useful and elegant new combinations of old ideas, with the occasional attention-grabbing paper when someone tries something that seems like it can&#x27;t work and it does, and we now have an entirely new thing in our toolbox.<p>Here is a highly unusual paper that showed up on HN recently, to little discussion.<p>It tackles a related problem to the OP paper but in a way that could not be more different from the current default of gradients, linear algebra, large training data volumes, hands free training.<p>It may end up being less impressive than it seems, but it is still a bit mind bending.<p><a href="http:&#x2F;&#x2F;journals.plos.org&#x2F;plosone&#x2F;article?id=10.1371&#x2F;journal.pone.0140866" rel="nofollow">http:&#x2F;&#x2F;journals.plos.org&#x2F;plosone&#x2F;article?id=10.1371&#x2F;journal....</a>