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No excuses necessary I think that's a great question. Yes, like in the article - you can remove extremely unlikely to be real things, unlikely to be real could
by Grimblewald 2y ago
No excuses necessary I think that's a great question. Yes, like in the article - you can remove extremely unlikely to be real things, unlikely to be real could be because of things such as being extremely conditional (pentavalent carbon for example) or simply implausible. The point is that by preventing the model from learning a distribution that captures the extremely rare though real instances of things, you end up with a distribution that departs from whatever the reality of the situation is. You end up with a biased model, and that is highly unlikely to be a good thing.
Bottom line, it is better to let a model learn the full distribution then clip predictions that come from extreme ends of the distribution, rather than preventing the model from considering these extremes in the first place. Either way you don't get predictions from the extremes, but the model allowed to consider extremes is more likely to do interesting things.
- Bluestein 2y agoMakes sense, thanks for taking the time.- > The point is that by preventing the model from learning a distribution that captures the extremely rare though real instances of things, you end up with a distribution that departs from whatever the reality of the situation is. You end up with a biased model, and that is highly unlikely to be a good thing. I (think I) understand. On top, the model being able to generate for conditions able to bring forth a "materially" (in realizable fact) unlikely condition, might yield results that are interesting, -particularly- because the conditions that it would be contingent upon -are- so hard, or unlikely in actual fact. Makes sense.-
- iboisvert 2y agoI think I may have heard of a similar problem in a different field, computational thermodynamics. Solutions to a set of equations may be real or complex. The nonlinear solvers worked with real numbers exclusively and from time to time would not converge to any solution. Apparently if one used a solver that worked with complex numbers, the solver would reach a solution in real space more often than exclusively real solvers. Of course solving was much more expensive. Your explanation seems to fit here too
- Grimblewald 2y agoInteresting, thanks for sharing! Do you have any papers or other publicly available works to share that discuss this? I'd be very interested in reading about it.
- iboisvert 2y agoI originally heard about this many years ago at an informal talk, but I was able to find this paper that sounds like the same problem and should have pretty good references https://www.sciencedirect.com/science/article/abs/pii/S009813540700292X https://www.sciencedirect.com/science/article/abs/pii/S00981...
- 11101010001100 2y agoI'm guessing the complex numbers add the right kind of 'fuzziness'. But please share any references if you have.