Learning Multi - Linear Representations for Efficient

We examine the class of multi-linear representations (MLR) for expressing probability distributions over discrete variables. Recently, MLRs have been considered as intermediate representations that facilitate inference in distributions represented as graphical models. We show that MLR is an expressive representation of discrete distributions and can be used to concisely represent classes of distributions which have exponential size in other commonly used representations, while supporting probabilistic inference in time that is linear in the size of the representation. Our key contribution is presenting techniques for learning bounded-size distributions represented using MLRs, which support efficient probabilistic inference. We then demonstrate experimentally that the MLR representations we learn supports accurate and very efficient inference.