Ivana Balazevic
@ibalazevic.bsky.social
Senior Research Scientist at Google DeepMind, working on Gemini.
PhD from University of Edinburgh.
ibalazevic.github.io
PhD from University of Edinburgh.
ibalazevic.github.io
Reposted by Ivana Balazevic
I think this comes down to the model behind p(x,y). If features of x cause y, e.g. aspects of a website (x) -> clicks (y); age/health -> disease, then p(y|x) is a (regression) fn of x. But if x|y is a distrib'n of different y's (e.g. cats) then p(y|x) is given by Bayes rule (squint at softmax).
December 2, 2024 at 8:20 AM
I think this comes down to the model behind p(x,y). If features of x cause y, e.g. aspects of a website (x) -> clicks (y); age/health -> disease, then p(y|x) is a (regression) fn of x. But if x|y is a distrib'n of different y's (e.g. cats) then p(y|x) is given by Bayes rule (squint at softmax).
Could you add me please? :)
November 24, 2024 at 8:33 PM
Could you add me please? :)
Me too please :)
November 22, 2024 at 12:30 AM
Me too please :)