3 ms·
Single label has been the applicable one for most of the applications we've tackled to date, but agreed that multi-label is also very important! More coming he
by ajratner 6y ago
Single label has been the applicable one for most of the applications we've tackled to date, but agreed that multi-label is also very important! More coming here soon...
- eggie5 6y agoThanks Alex. I'm sure you can relate that when you have a an unbounded input distribution (like w/ user-interaction systems), defining that other class w/ current snorkel is difficult/impossible.
- ajratner 6y agoYeah definitely- and would love to chat sometime, as this is a space I've at least had less direct hands-on interaction with. There's a line of work in the ML literature on "Positive unlabeled (PU) learning"--basically, setting where there are only positive labels or abstains--with a lot of theoretical ties to what our stuff rests on, I think a tie in here is interesting. Of course, most of these approaches rely on some (to varying degrees) hidden and very strong distributional assumption... anyway looking forward to a chat!
- eggie5 6y agoThanks for the lead on PU Learning. I signed up for a demo of the new platform, looking forward to chatting. Me and a colleague from work spoke w/ Henry last year about a potential partnership but I guess it got lost in the mix...