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afireohno t1_j9230xx wrote

There are two lines of work that come to mind you might be interested in.

  1. Geometric deep learning primarily studies various types of invariances (translation, permutation, etc) that can be encoded in DL architectures.
  2. Algorithmic alignment studies the relationship between information flow in classical algorithms and DL architectures and how "aligning" the latter to the former can improve performance.

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