in domains where feature order has meaning, CNN seem to do really well.
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while brain connections are way more complex, are there any evidence for a similar structure in brain?
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Yeah, I think so, I've been advocating for ConvNets for language for a long time. Glad to see some vindication :)
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I think the reason is that information are processed spatially in the brain, or at least CNNs approximate this somewhat well.
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at least the ones our own minds are capable of performing and probably a large percentage we aren't capable of
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someone is literally attempting to solve *every* pattern matching problem in spacetime with dilated CNNs:https://www.reddit.com/r/MachineLearning/comments/5agopr/research_161010099_neural_machine_translation_in/d9gfhzb/ …
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It's been available in Keras for 6 months as a layer (both backends). That was long before it started gaining attention.
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To a hammer everything looks like a nail. Don't you think?
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interesting that CNNs work when conv assumption doesnt hold. using VGG on freq axis of spectrograms.https://arxiv.org/abs/1609.09430
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Not likely. Not for problems with low self-similarity or where invariance assumptions dont hold approximately. Nor when
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computation exceeds bounds of what conv net can capture or where minute details matter.Climate,weather&complex sys.Hard proteomics
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