High-Dimensional Structural Inference for Non-Linear Deep Markov or State Space Time Series Models

In many applications, a time series of high-dimensional latent vector variables is observed indirectly from noisy measurements. The data are used to predict future failures, and the system can respond accordingly. This project will investigate deep Markov models (DMMs), in which an inference network approximates a posterior probability for the time-dynamics of latent variables by running a multi-layer perceptron (MLP) neural network. We will implement and develop a DMM system that can cope with various types of statistical structure among the features, and pay close attention to scaling the computation as the dimensionality increases.

Project PIs: Dror Baron, Rhett Davis, Paul Franzon

Research Thrust: Theory and Machine Learning Efficiency

Research Timeline Jan 1, 2019 – Dec 31, 2019