This appears to be a high-quality book report. Thanks. I didn’t see anywhere the ‘because’ is demonstrated. Is it proved in the citations or do we just have ‘plausibly because’?
Physics experiences in optimizing free energy have long inspired ML optimization uses. Did physicists playing with free energy lead to new optimization methods or is it just something people like to talk about?
This appears to be a high-quality book report. Thanks. I didn’t see anywhere the ‘because’ is demonstrated. Is it proved in the citations or do we just have ‘plausibly because’?
The because ends up taking a few dozen pages to establish in Watanabe 2009 (and only after introducing algebraic geometry, empirical processes, and a bit of complex analysis). Anyway, I thought it best to leave the proof as an exercise for the reader.
Physics experiences in optimizing free energy have long inspired ML optimization uses. Did physicists playing with free energy lead to new optimization methods or is it just something people like to talk about?
I’m not quite sure what you’re asking. Like you say, physics has a long history of inspiring ML optimization techniques (e.g., momentum/acceleration and simulated annealing). Has this particular line of investigation inspired new optimization techniques? I don’t think so. It seems like the current approaches work quite well, and the bigger question is: can we extend this line of investigation to the optimization techniques we’re currently using?
This appears to be a high-quality book report. Thanks. I didn’t see anywhere the ‘because’ is demonstrated. Is it proved in the citations or do we just have ‘plausibly because’?
Physics experiences in optimizing free energy have long inspired ML optimization uses. Did physicists playing with free energy lead to new optimization methods or is it just something people like to talk about?
The because ends up taking a few dozen pages to establish in Watanabe 2009 (and only after introducing algebraic geometry, empirical processes, and a bit of complex analysis). Anyway, I thought it best to leave the proof as an exercise for the reader.
I’m not quite sure what you’re asking. Like you say, physics has a long history of inspiring ML optimization techniques (e.g., momentum/acceleration and simulated annealing). Has this particular line of investigation inspired new optimization techniques? I don’t think so. It seems like the current approaches work quite well, and the bigger question is: can we extend this line of investigation to the optimization techniques we’re currently using?