if nobody bites on question 3, it will make my next medium post a lot easier to write :)https://twitter.com/GaryMarcus/status/1209640096900812800 …
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Your definitions are good but I prefer clusters induced by definitions to be context dependent. For example, I can have a giant network where each function is the dot product & each edge a weight. It would be differentiable but it can be rewritten as a single linear transform.
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Convnets, Simple RNNs & even MLPs can be deep yet need not be coupled to an attention mechanism Regarding vector spaces: lots fit here (linear models trivially). You can have recursive probabilistic programs that are effectively over a vector space & deep in any meaningful sense
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