Subject
2 entries
Category Theory
Bookmarks
Bayesian Machine Learning via Category Theory
Culbertson and Sturtz's 2013 paper applying category theory to Bayesian machine learning — using the Kleisli category of the Giry monad to formalize supervised learning, stochastic processes as priors, and the Kalman filter. Heavy theory, but part of the broader push to give probabilistic ML rigorous foundations.
Functors, Applicatives, and Monads in Pictures
Aditya Bhargava's illustrated guide to functors, applicatives, and monads using colorful box-and-function diagrams. The best visual introduction to these Haskell/category theory concepts for programmers coming from imperative languages.