ABSTRACT
This paper proposes density estimation as a feasible approach to the wide class of learning problems where traditional function approximation methods fail. These problems generally involve learning the inverse of causal systems, specifically when the inverse is a non-convex mapping. We demonstrate the approach through three case studies: the inverse kinematics of a three-joint planar arm, the acoustics of a four-tube articulatory model, and the localization of multiple objects from sensor data.
