AdFew4357 t1_j7ztran wrote on February 10, 2023 at 4:14 PM

Reply to comment by jimmymvp in [D] Critique of statistics research from machine learning perspectives (and vice versa)? by fromnighttilldawn

Stats is finding interpretable ways to look at and mode data that ML plug and chug cs people don’t do

jimmymvp t1_j806dx2 wrote on February 10, 2023 at 5:36 PM

Just communicating what I've heard. Nevertheless, I think the whole interpretable ML community (at the very least) would disagree with you on this one :). Reducing ML to "plug and chug" is well... Speaks for itself :D

AdFew4357 t1_j806plm wrote on February 10, 2023 at 5:38 PM

The whole landscape of ML research is a hunt to chase SOTA by tweaking an architecture here or using a different optimizer there and then squeezing out 0.2% accuracy on some well known imaging dataset in an attempt to churn out papers. That’s not science if you ask me.

jimmymvp t1_j83v503 wrote on February 11, 2023 at 1:16 PM

I'm not sure if you have a good overview of ML research if this is your claim. Sounds like you've read too many blog posts on transformers. I'd suggest going through some conference proceedings to get a good overview, there's some pretty rigorous (not just stats) stuff out there. I agree though that there is a substantial subset of research in ML that works towards tweaking and pushing the boundaries of what can be achieved with existing methods, which is for me personally exciting to see! A lot of cool stuff came out of scaling up and tweaking the architectures.