# Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models

arXiv: https://arxiv.org/abs/1908.07699 · HTML: https://mlsanity.com/paper/1908.07699/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [SiamEvent: Event-based Object Tracking via Edge-aware Similarity Learning with Siamese Networks](https://mlsanity.com/paper/2109.13456/) (2021)

## Abstract (arXiv)

Tensor methods have become a promising tool to solve high-dimensional problems in the big data era. By exploiting possible low-rank tensor factorization, many high-dimensional model-based or data-driven problems can be solved to facilitate decision making or machine learning. In this paper, we summarize the recent applications of tensor computation in obtaining compact models for uncertainty quantification and deep learning. In uncertainty analysis where obtaining data samples is expensive, we show how tensor methods can significantly reduce the simulation or measurement cost. To enable the deployment of deep learning on resource-constrained hardware platforms, tensor methods can be used to significantly compress an over-parameterized neural network model or directly train a small-size model from scratch via optimization or statistical techniques. Recent Bayesian tensorized neural networks can automatically determine their tensor ranks in the training process.