running some method comparisons, aiming to demonstrate superiority of one parameter set amusingly running into the problem not that the experimental parameters don't improve things, but that the baseline parameterization is implausibly garbage
I'm so used to the ML paradigm at this point that I straight up automatically disbelieve all interpretable coefficients
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This is basically the correct attitude to have
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