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Here's what I think it is or could be - machine learning where backpropagation-like training doesn't just optimize weights/parameter of fixed functions, but also figures out which functions to use (among a specified set) and in whatever sequence. The "which" question can be solved with gating
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I completely agree with you and reverted the reversionĀ :-) To be clear, I think it's reasonable that the āāPā paper used the abbreviation, yet it does not seem to be a standard and so there's no justification for putting it throughout the article.
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Iām a CS major, but after reading the article, I have NO clue what the lemma is about. I canāt even begin to imagine what differentiation could mean in a programming language context. This article could use some work...
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This distinction seems a bit arbitrary and e.g. Myia doesn't use it, it is just source-to-source vs operator overloading. IMO the distinction should be removed, just discuss the approaches chronologically. Thoughts?
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Yes. I happened on the article serendipitously. And I leave no wiser than when I came. CS background too. Some clue as to both the meaning and the benefits or purpose of differentiation in this context is required.
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An anonymous user reverted my change removing the abbreviation āāPā from this page. To be clear, I donāt think this abbreviation meets
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