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Making assumptions about the structure of correlations in the data will *reduce* the expressiveness of the model (that's the point: dramatically reduce the search space to make search more data-efficient and faster). Which is why it is important to make *correct* assumptions.https://twitter.com/AmirRosenfeld/status/1033850972299321344 …
To use a trivial example, don't use convolution layers on structured data, as you will be ignoring many important correlations across features that are not spatially close in your original encoding of the data, but that are close in information space.
Good observation.
A good approach but what do you think is the alternative? We almost use conv layers in all cases to promote the most discriminative features ..
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