CDS&E-MSS: Sparsely Activated Bayesian Neural Networks from Deep Gaussian Processes

Texas A&M Engineering Experiment Station, College Station TX

Investigators

Abstract

This Computational and Data-Enabled Science and Engineering in Mathematical and Statistical Sciences (CDS&E-MSS) project aims to develop a systematic approach for constructing deep Bayesian neural networks that are both computationally efficient and amenable to model designs. The results of this project are expected to lead to significant improvements in terms of accuracy, confidence, and efficiency over the current machine learning and artificial intelligence models. Open-source software will be developed during the course of this project, providing accessible tools for researchers and practitioners. This study is expected to impact a wide range of applications including self-driving cars, medical diagnostics, and trading and finance. The investigators are also committed to promoting STEM education and research opportunities for minority and underrepresented groups, and this project also provides research training opportunities for graduate students. The research is expected to advance deep Gaussian process models and relevant Bayesian neural networks in several key areas, including theory and algorithmic development, their applications to generative tasks, and software development and validation. The research and development plan mainly consists of the following three components: 1) to better understand the properties of deep tensor Markov Gaussian process models and to construct a broader class of models that have more complex architectures but can still be approximated by deep Bayesian neural networks with sparse structure; 2) to explore the stochastic nature of deep Gaussian process models to create novel conditional generative models; and 3) to develop software tools and to validate the project outcomes using real-world datasets from the state-of-the-art wireless communication and computer vision systems. This award by the NSF Division of Mathematical Sciences is jointly supported by the Division of Civil, Mechanical, and Manufacturing Innovation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.