Blog

Learn About Our Meetup

4200+ Members

[D] Modern applications of statistical learning theory?

I was reading about concentration of measure related stuff recently and was curious whether anyone knows whether this material is still applicable to ‘deep learning’ models. By statistical learning theory I mean stuff like VC / Rademacher bounds etc.

If it is, can anyone point to any research papers on this topic?

From my naive understanding, because these bounds relate to the worst-case scenario the union bound may be excessively pessimistic in terms of the number of training examples required.

submitted by /u/tensorflower
[link] [comments]

Next Meetup

 

Days
:
Hours
:
Minutes
:
Seconds

 

Plug yourself into AI and don't miss a beat

 


Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.