Modularity is a critical design principle for both software systems and artificial intelligence (AI).

Autonomous agents operating in real-world scenarios frequently encounter uncertainty and make decisions based on incomplete information.

A fundamental structural model for data is that the data points lie close to an unknown subspace, meaning that the matrix created by concatenating the data vectors has low rank. We address a particular low-rank matrix recovery problem where we wish to recover a set of vectors from a low-dimensional subspace after they have been individually compressed (or "sketched").

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Inference in the introduced linear Gaussian SSM is straightforward and can be performed using Kalman prediction and observation updates.