Systems and methods for using deep machine learning to problems corresponding to non-linear generator functions

Abstract

Systems, apparatuses, methods, and computer program products are disclosed for determining a value corresponding to a composite object. Paths are determined based on initial values of underlying items of the composite object and random numbers. A DNN is trained by determining a final value for each path based on a final set of items corresponding to the path at the final time, iterating the value backward in time using a non-linear generator function from the final value to an initial value, defining a set of initial values comprising the initial value determined for each path and determining a statistical measure based on the set of initial values, and modifying parameters of the DNN based on the statistical measure. Value information comprising the value for the composite object at one or more times is determined based on output of the DNN. The value information is provided such that a user computing device receives it.