Learners' Languages

David I. Spivak
(Topos Institute)

In "Backprop as functor", the authors show that the fundamental elements of deep learning—gradient descent and backpropagation—can be conceptualized as a strong monoidal functor Para(Euc)–>Learn from the category of parameterized Euclidean spaces to that of learners, a category developed explicitly to capture parameter update and backpropagation. It was soon realized that there is an isomorphism Learn=Para(Slens), where Slens is the symmetric monoidal category of simple lenses as used in functional programming.

In this note, we observe that Slens is a full subcategory of Poly, the category of polynomial functors in one variable, via the functor A|->Ay^A. Using the fact that (Poly,ø) is monoidal closed, we show that a map A–>B in Para(Slens) has a natural interpretation in terms of dynamical systems (more precisely, generalized Moore machines) whose interface is the internal-hom type [Ay^A,By^B].

Finally, we review the fact that the category p-Coalg of dynamical systems on any p in Poly forms a topos, and consider the logical propositions that can be stated in its internal language. We give gradient descent as an example, and we conclude by discussing some directions for future work.

In Kohei Kishida: Proceedings of the Fourth International Conference on Applied Category Theory (ACT 2021), Cambridge, United Kingdom, 12-16th July 2021, Electronic Proceedings in Theoretical Computer Science 372, pp. 14–28.
Published: 3rd November 2022.

ArXived at: https://dx.doi.org/10.4204/EPTCS.372.2 bibtex PDF

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