Learning about an exponential amount of conditional distributions

Abstract

We introduce the Neural Conditioner (NC), a self-supervised machine able to learn about all the conditional distributions of a random vector $X$. The NC is a function $NC(x\cdot a,a,r)$ that leverages adversarial training to match each conditional distribution $P(X_r|X_a=x_a)$. After training, the NC generalizes to sample from conditional distributions never seen, including the joint distribution. The NC is also able to auto-encode examples, providing data representations useful for downstream classification tasks. In sum, the NC integrates different self-supervised tasks (each being the estimation of a conditional distribution) and levels of supervision (partially observed data) seamlessly into a single learning experience.

8 pages, 7 figures

References in corpus (2)

• Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net
• A Universal Marginalizer for Amortized Inference in Generative Models