  Neural networks leverage robust internal representations in order to
generalise. Learning them is difficult, and often requires a large training set
that covers the data distribution densely. We study a common setting where our
task is not purely opaque. Indeed, very often we may have access to information
about the underlying system (e.g. that observations must obey certain laws of
physics) that any "tabula rasa" neural network would need to re-learn from
scratch, penalising performance. We incorporate this information into a
pre-trained reasoning module, and investigate its role in shaping the
discovered representations in diverse self-supervised learning settings from
pixels. Our approach paves the way for a new class of representation learning,
grounded in algorithmic priors.

Machine Learning;Artificial Intelligence;Machine Learning