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* proofs as types. | ||
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We can consider the type of types, UU the universe of universes. | ||
The types can related in paths of type level or dependent type programming | ||
in haskell for example. | ||
We can convert unimath proofs to ocaml and haskell via metacoq extractions. | ||
we can train a detailed model to reverse this extraction, | ||
a automatic reverse extractor that is trained on the extraction. | ||
We can use this method to convert values from trained model weights | ||
using symbolic regression and program generation. | ||
These generated programs can be apprehended as types. | ||
the types can be bound into the proof engine. | ||
this shows the connection of a value learned by a neural network | ||
and a values held in the proof engine as a constructed proof in metacoq. | ||
That means we can also train a neural network to reverse this process. | ||
we can go from unimath proofs to templates to create preptrained neural networks and back. |