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This is something I already suspected since you can encode piece-wise linear function-based neural networks (of a reasonable size) as mixed-integer problems (MI

This is something I already suspected since you can encode piece-wise linear function-based neural networks (of a reasonable size) as mixed-integer problems (MIP). Since MIP is NP-complete, that means you could also translate the neural network more painfully into SAT, giving you all the interpretability you could possible want... Except I'm not too sure how "interpretable" the results really would be in most cases. What questions would you pose to an ML model recognizing images or audio?

rrobukef 4y ago
Second order questions (i.e. get a really good machine): What is the minimal difference in input needed for class A to be recognized as class B? What the (an) input closest to all classes and what is the distance to each? Are there weights for this smaller NN that remain equivalent to the given NN?
- sirwhinesalot 4y ago
  That makes a lot of sense, thanks!