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DOE OSTI · 2283538

Bayesian Tensor Decompositions for Scalable Supervised Learning of Scientific Data (Final Report)

Gorodetsky, Alex
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Abstract

In this document we highlight the detailed accomplishments and progress that we have made in this period. This progress seeks to address the three main objectives to provide new algorithms for quantifying uncertainty in low-multilinear-rank models and to leverage them for data analysis. These include: (1) develop probabilistic models for low-multilinear-rank functions; (2) develop a suite of Bayesian learning approaches to learn the probabilistic models from data; (3) apply the techniques on challenging problems arising in DOE-relevant applications.

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97 MATHEMATICS AND COMPUTINGBayesian inferenceincremental tensor decompositionsLow rank tensorsprobabilistic modelstensor networks

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Gorodetsky, Alex. 2024-02-01. Bayesian Tensor Decompositions for Scalable Supervised Learning of Scientific Data (Final Report). https://doi.org/10.2172/2283538

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