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Vapnik: https://www.cs.princeton.edu/courses/archive/spring13/cos511/handouts/vapnik-slides.pdf https://www.cs.princeton.edu/courses/archive/spring13/cos511...
by gwern 3y ago
Vapnik: https://www.cs.princeton.edu/courses/archive/spring13/cos511/handouts/vapnik-slides.pdf https://www.cs.princeton.edu/courses/archive/spring13/cos511... https://engineering.columbia.edu/files/engineering/vapnik.pdf https://engineering.columbia.edu/files/engineering/vapnik.pd... https://www.learningtheory.org/learning-has-just-started-an-interview-with-prof-vladimir-vapnik/ https://www.learningtheory.org/learning-has-just-started-an-... https://nautil.us/teaching-me-softly-234576/ https://nautil.us/teaching-me-softly-234576/
The main paper: https://gwern.net/doc/reinforcement-learning/exploration/active-learning/2015-vapnik.pdf https://gwern.net/doc/reinforcement-learning/exploration/act...
It sounds kinda crazy (is there really that much far transfer?), but you know, I think it would work... He just needed to use LLMs instead: https://arxiv.org/abs/2309.10668#deepmind https://arxiv.org/abs/2309.10668#deepmind
- YeGoblynQueenne 3y agoYeah, that's one of the papers in that line of research by Vapnik. He's got a few with similar content. Visually, it's not the paper I remember, I'll have to read it again to be sure. If I remember correctly, Vapnik's point is, we know that Big Data Deep Learning works; now, try to do the same thing with small data. Very much like my point that capabilities of models are not improving, only the scale increasing.