Re:The Modern Mathematics of Deep Learning
by iNic on Saturday June 12, 2021 @05:46PM
(#27487678)
This is always difficult to answer, and it will probably be a mixture of many, however I am currently following categorical approaches to machine learning. Category Theory is the area of mathematics that studies composable structures, i.e. like layers in a deep network. It is very abstract and was invented to solve problem in algebraic geometry, but has been fruitful in other areas as well.
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Re:The Modern Mathematics of Deep Learning
by joe_the_user on Saturday June 12, 2021 @07:35PM
(#27488379)
That you this illustrates that the situation today is "take whatever math-stuff you have, throw it against neural networks and see what you get". IE, I'm pretty sure not much progress has been made with category theory and neural networks - but you might be the first.
I've seen differential equations, Markov chains, differential geometry and other stuff. We might be in heady days before the "big breakthrough" is made. But these constructs might be inherently pathological (even then, non-pathological variants might be possible).
It’s good for paper publication, and for public sector. Take an obscure area of math and throw it against latest trend.
It’s a question if it’s useful.
Re:The Modern Mathematics of Deep Learning
by SubiculumCode on Saturday June 12, 2021 @10:14PM
(#27489203)
It can be useful for innovation in the aggregate.
Re:The Modern Mathematics of Deep Learning
by convexity123 on Saturday June 12, 2021 @06:34PM
(#27487968)
Could you give some favourite references, some use of category theory in ML which gives good results compared to standard approaches?
Is there a group doing this in Zurich?