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[matnormal] Matrix-normal (dual probabilistic) SRM #490

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@mshvartsman mshvartsman commented Jan 28, 2021

This is a TF2 port of the matrix-normal SRM code we used in https://arxiv.org/abs/1711.03058. It's not quite ready for merge but I'm putting up the PR to start discussion and maybe get another pair of eyes on the code. In contrast to the code we used for the paper (https://github.com/mshvartsman/brainiak/blob/feature-dpmnsrm/brainiak/matnormal/dpmnsrm.py), this is runnable in current TF and includes asserts to ensure the monotonicity of the ECM(E) algorithm. I also fixed at least one math bug (there's a typo in the paper that made it into the code). For now I'm excluding all model variants that require pymanopt (until pymanopt/pymanopt#126 is merged).

The bad news: those asserts fail on the update for subject-specific noise scalers (rhoprec) and I can't figure out why. I'd love another pair of eyes on this so we can get it merged.

Important disclaimer: This work is all done on my own time and my own equipment, based on a paper I wrote while at Princeton, and is unrelated to my current work with my employer.

Mike Shvartsman and others added 30 commits March 2, 2018 16:12
Update with mainline repo
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mihaic commented Feb 2, 2021

Thank you, @mshvartsman! Could you please name a preferred owner of that second pair of eyes?

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Maybe @lcnature or @szorowi1? Or, who is the current primary dev on the various other SRM flavors? Is it @TuKo?

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lcnature commented Feb 4, 2021

I would encourage others to review, as I am still owing review for another PR.

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mihaic commented Mar 8, 2021

@szorowi1, could you please review this pull request?

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4 participants