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> In theory nothing prevents you from having a cascade-like approach with deep learning. Sure, people do that in DL. There are plenty of 'adaptive' or 'anytime
by gwern 7y ago
> In theory nothing prevents you from having a cascade-like approach with deep learning.
Sure, people do that in DL. There are plenty of 'adaptive' or 'anytime' or mixture-of-experts architectures for classification. And lots of things take that cascade approach naturally, like any object detection or localization NN: you make a bunch of proposals and check those rectangles; the more computation you have, the more you can check.
- dr_zoidberg 7y agoRight, I meant the specific approach of making a cascade "outside the model" by programming it by hand, as in: clf1 = FirstTierModel(...) clf2 = SecondTierModel(...) clf3 = ThirdTierModel(...) # and then imagine downwards pred1 = clf1.infer(data) if pred1 == False: return False pred2 = clf2.infer(data) if pred2 == Fase: return False # ... and so on (did a bit of a python-y syntax) Then I went on with how it can be embedded inside the architecture and that's a better approach.