NitroXSC
NitroXSC t1_j9k09wt wrote
Reply to comment by hpstring in [D] "Deep learning is the only thing that currently works at scale" by GraciousReformer
> Q2: Probably there are other classes but they haven't been discovered or are only at the early age of research.
I think there are many different classes that would work but current DL is based in large parts on matrix-vector operations which can be implemented efficiently on current hardware.
NitroXSC t1_j70z581 wrote
Reply to comment by mongoosefist in [R] Extracting Training Data from Diffusion Models by pm_me_your_pay_slips
https://en.m.wikipedia.org/wiki/Differential_privacy
Differential privacy has multiple methods of recovering the input data from output data, but that is most often only quite simple models. Hence it might be possible.
NitroXSC t1_j6wdaje wrote
> Compute CLIP embeddings for the images in a training dataset.
A good follow-up question is to ask if it would be possible to recover a lot of the training data if you don't know the training data a priori.
NitroXSC t1_jbdp6ge wrote
Reply to [R] Analysis of 200+ ML competitions in 2022 by hcarlens
Interesting meta-study with many remarkable trends.
This is seen from the competitor's side, but what correctly the best website to set up a simple prediction competition? I'm asking this since I'm planning on creating a small competition for students of one of the courses given (no large files needed).