Backprop as Functor: A compositional perspective on supervised learning
A supervised learning algorithm searches over a set of functions A→B parametrised by a space P to find the best approximation to some ideal function f:A→B. It does this by taking examples (a,f(a))∈A×B, and updating the parameter according to some rule. We define a category where these update rules m...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Association for Computing Machinery
2020
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| Online Access: | https://hdl.handle.net/1721.1/123513 |