10-15 yrs ago, appears ~everybody talking cortical implementation of belief prop, ~nobody talking cortical implementation of back prop: https://scholar.google.com/scholar?q=related:CP4ffwsbGlcJ:scholar.google.com/&scioq=Cortical+circuitry+implementing+graphical+models&hl=en&as_sdt=0,5 … (not saying this is bad -- both very inspirational algorithms -- just interesting)
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Seriously though, approximate backprop seems simpler than approximate PGM inference... and many probabilistic inference problems can be re-framed as neural nets as the field is doing now
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And there are things like:https://www.nature.com/articles/s41467-017-00181-8 …
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New conversation -
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I think we probably need 3-4 entirely new paradigms on the scale of deep learning, graphical models, or evolutionary computing. This will likely be a multigenerational project.
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