r/MachineLearning - [D] Machine Learning - WAYR (What Are You Reading) - Week 72

I've been idly wondering lately about the problem of identifying high value samples to obtain for improving models, which seems to get at something similar under uncertainty. It isn't necessarily going to be economical to do an exhaustive sampling of whatever you're interested in, but collecting a few strategic datapoints could be relatively affordable and help a lot with inference. I also was wondering if some kind of hypothesis falsification module could be stapled onto gradient descent algorithms somehow. In terms of simulated annealing, because that's mentally easier for me, the idea would be that we want the temperature of nonlocal jumps to be hotter when the machine is making failed guesses, and we want it to be cooler when the gradient is behaving like the falsification module expects. The motivation for this is just that for inference, a lot of the time it is easier to learn things if you go out of your way to test your assumptions. Just having those assumptions be consistent with your observations is only a weak test of their value.