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A good deep learning architecture is one that introduces correct priors about the problem at hand -- in particular, about the structure of the correlations found in the data.
For instance, convolution is preferable to dense layers for data dimensions that are locally autocorrelated and translation-invariant. And depthwise separable convolutions are superior to convolution for any data that is locally autocorrelated and where channels are decorrelated.
(Which is naturally the case past the first layer of any convnet.)
Bayesian approach?
Seems more like Bayesian terminology in expressing an observation. Though you can choose to discuss scientific research as a Bayesian process, Architecture designers are usually not actually following any discernibly Bayesian approach.
Where would you draw the line between the correct prior and the expressiveness of the model?
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