- Abstract: Traditionally, unsupervised representation learning is used to discover underlying regularities from raw sensory data without relying on labeled data. A great number of algorithms in this field resorts to utilizing proxy objectives to facilitate learning. Further, learning how to act upon these regularities is left to a separate algorithm. Neural encoding in biological systems, on the other hand, is optimized to represent behaviorally relevant features of the environment in order to make inferences that guide successful behavior. Evidence suggests that neural encoding in biological systems is shaped by such behavioral objectives. In our work, we propose a model of inference-driven representation learning. Rather than following some auxiliary, a priori objective (e.g. minimization of reconstruction error, maximization of the fidelity of a generative model, etc.) and indiscriminately encoding information present in an observation, our model learns to build representations that support accurate inferences. Given a set of observations, our model encodes underlying regularities that de facto are necessary to solve the inference problem in hand. Rather than labeling the observations and learning representations that portray corresponding labels or learning representation in a self-supervised manner and learning explicit features of the input observations, we propose a model that learns representations that implicitly shaped by the goal of correct inference.