Supervised structure learning
Karl J. Friston Lancelot Da Costa Alexander Tschantz Alex Kiefer Tommaso Salvatori Victorita Neacsu Magnus Koudahl Conor Heins Noor Sajid Dimitrije Markovic Thomas Parr Tim Verbelen Christopher L Buckley
This paper concerns structure learning or discovery of discrete generative
models. It focuses on Bayesian model selection and the assimilation of training
data or content, with a special emphasis on the order in which data are
ingested. A key move - in the ensuing schemes - is to place priors on the
selection of models, based upon expected free energy. In this setting, expected
free energy reduces to a constrained mutual information, where the constraints
inherit from priors over outcomes (i.e., preferred outcomes). The resulting
scheme is first used to perform image classification on the MNIST dataset to
illustrate the basic idea, and then tested on a more challenging problem of
discovering models with dynamics, using a simple sprite-based visual
disentanglement paradigm and the Tower of Hanoi (cf., blocks world) problem. In
these examples, generative models are constructed autodidactically to recover
(i.e., disentangle) the factorial structure of latent states - and their
characteristic paths or dynamics.