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Maybe I'm missing something? To me, that gif illustrates sets of samples that are on the surface compatible with the the same Gaussian distribution, even if a
by macawfish 9y ago
Maybe I'm missing something? To me, that gif illustrates sets of samples that are on the surface compatible with the the same Gaussian distribution, even if a Gaussian distribution is "obviously" lacking.
- wenc 9y agoI think 0xBABAD00C's comment is on the money. The gif is almost a non-sequitur and does not seem to support the conclusion the article is trying to reach. The second part of the article makes a claim that is worth discussing -- that probabilistic inference does not work well in nonlinear domains. I think this is true in one sense, but in many practical situations we can usually to get something to work well enough for it to be useful. Assuming we can (approximately) split up the domain into multiple regions and get probability distributions for each region (with transition functions between them), we can continue to make inferences under nonlinearity. That said, this is a concession. Perhaps there are techniques in deep learning that more general in nature.