> don't know if you'll read this I did read it ;) Because I'm very much interested in this entire topic. > I hope I can encourage you to think more broadly a

> don't know if you'll read this I did read it ;) Because I'm very much interested in this entire topic. > I hope I can encourage you to think more broadly about what "differentiable programming" means I'm trying to, but I find it hard. My stance was that differentiable programming seemes like this theory for which only a single example (namely autodiff) existed, as you also said ("autodiff has mostly developed in practical usage, so the use cases are front-running the theory"). But this entire comment of yours really clarified some things for me. > The main paper I linked [2] is not about autodiff at all. >The quote you cite from [3] is easily misunderstood without that context You finally convinced me to have a detailed look at this. Thank you for providing the context. > Conditionals and loops are possible in [2] since it allows church numerals and fixed point combinators but it introduces a nondeterministic sum [...] and is difficult to operationalize. That's what I meant by "wildly uncomputable". I think I may have misunderstood some of your previous comments (and perhaps vice versa) as it now dawns on me that you use a vocabulary than comes (I guess?) from PL theory and is very different from the one I'm used to, as a mathematician versed in analysis. I'll re-read them. > Daniel Murfet et al [6] have some related work more directly in the context of machine learning. I'm actually aware of Daniel Murfet but haven't read his work from last years. Did you have a specific paper from him in mind?