zlacker

Ah, Dirichlet Processes, such lovely things.

Reading this paper, I was struck by how obvious most of the solutions were given my own background from grad school benchmarking quantum annealers and other classical solvers for spin lattices (mostly thermal sampling inspired approaches). I'd argue one could do an even better job than the analysis in Anthropic's paper, but it's astonishing how basic questions like "well how sure are we this is real?" just aren't asked seemingly in ML papers.

I developed a passion for Bayesian statistics approaches in grad school, and had a lovely time specifically thinking quite a bit about DPs, Bayesian bootstraps, etc. I'm sorry your paper is bouncing around. I think folks underestimate these days the value of really thinking about what you know and how you know it, and how to really model uncertainty, and definitely underrate non-DL approaches to problems.

>>joshjo+(OP)
Thanks, yes, lot of good ideas in ML seem to be slowly vanishing from the collective awareness. I have nothing against the current spate of methodologies which are empirically great - and if one needs proof, I am a "happy customer" at my day job which is mostly DL and a lot of LLMs - but it seems we are buying into a world where it is one versus the other. And this it need not be. Great ideas are great ideas irrespective of age and there is value in preserving them.

Anyway, since this thread surprisingly evoked a mini-discussion on Dirichlet Processes (DP), if someone needs an intro, I have tried to balance math and intuition in a description in my thesis: Section 2.2 in [1].

[1] https://drive.google.com/file/d/1zf_MIWyLY7nxEr5UioUQ7KhOQ1_...

EDIT: I looked at the description and I confess it still has a lot of math (since it is part of thesis). I will probably translate this to be more friendly and put it on my blog.