Tensor Train Accelerated Solution of Volume Integral Equation for 2-D Scattering Problems and Magneto-Quasi-Static Characterization of Multiconductor Transmission Lines

Chen, Zhuotong; Zheng, Shucheng; Okhmatovski, Vladimir

doi:10.1109/tmtt.2019.2908368

Cited by 8 publications

(1 citation statement)

References 34 publications

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“…Tensor techniques can be employed to achieve both goals. Representative tensor techniques for the first goal include [25]- [27]. In this paper, we focus on tensor techniques for uncertainty and variability analysis as summarized in Table I, and we elaborate their key ideas below.…”

Section: A Data-expensive Eda Problemsmentioning

confidence: 99%

Tensor Methods for Generating Compact Uncertainty Quantification and Deep Learning Models

Cui

Hawkins

Zhang

2019

2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

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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 overparameterized neural network model or directly train a smallsize model from scratch via optimization or statistical techniques. Recent Bayesian tensorized neural networks can automatically determine their tensor ranks in the training process.

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Section: A Data-expensive Eda Problemsmentioning

confidence: 99%