2021-03-022021-03-022020-11-24Patel, Ankit B. and Baraniuk, Richard G., "Automated compilation of probabilistic task description into executable neural network specification." Patent US10846589B2. issued 2020-11-24. Retrieved from <a href="https://hdl.handle.net/1911/110123">https://hdl.handle.net/1911/110123</a>.https://hdl.handle.net/1911/110123A mechanism for compiling a generative description of an inference task into a neural network. First, an arbitrary generative probabilistic model from the exponential family is specified (or received). The model characterizes a conditional probability distribution for measurement data given a set of latent variables. A factor graph is generated for the generative probabilistic model. Each factor node of the factor graph is expanded into a corresponding sequence of arithmetic operations, based on a specified inference task and a kind of message passing algorithm. The factor graph and the sequences of arithmetic operations specify the structure of a neural network for performance of the inference task. A learning algorithm is executed, to determine values of parameters of the neural network. The neural network is then ready for performing inference on operational measurements.54engUtility patentUS10846589B2