Characterizing the invariances of learning algorithms using category theory
@article{Harris2019CharacterizingTI, title={Characterizing the invariances of learning algorithms using category theory}, author={Kenneth D. Harris}, journal={ArXiv}, year={2019}, volume={abs/1905.02072} }
Many learning algorithms have invariances: when their training data is transformed in certain ways, the function they learn transforms in a predictable manner. Here we formalize this notion using concepts from the mathematical field of category theory. The invariances that a supervised learning algorithm possesses are formalized by categories of predictor and target spaces, whose morphisms represent the algorithm's invariances, and an index category whose morphisms represent permutations of the…
2 Citations
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