  We propose a simple and efficient algorithm for learning sparse invariant
representations from unlabeled data with fast inference. When trained on short
movies sequences, the learned features are selective to a range of orientations
and spatial frequencies, but robust to a wide range of positions, similar to
complex cells in the primary visual cortex. We give a hierarchical version of
the algorithm, and give guarantees of fast convergence under certain
conditions.
