Tensor decomposition to compress convolutional layers in deep learning-Reference-Cited by-同舟云学术

Tensor decomposition to compress convolutional layers in deep learning

Published:2021-04-16 Issue: Volume: Page:1-60
ISSN:2472-5854
Container-title:IISE Transactions
language:en
Short-container-title:IISE Transactions

Author:

Wang Yinan¹^ORCID,Guo Weihong “Grace”²^ORCID,Yue Xiaowei¹^ORCID

Affiliation:

1. Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

2. Department of Industrial and Systems Engineering, Rutgers University, New Brunswick, NJ, USA

Funder

Division of Civil, Mechanical and Manufacturing Innovation

U.S. Department of Defense

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/24725854.2021.1894514

Reference36 articles.

1. Calvi, C.G., Moniri, A., Mahfouz, M., Zhao, Q. and Mandic, D.P. (2020) Compression and interpretability of deep neural networks via Tucker tensor layer: From first principles to tensor valued back-propagation. ArXiv: 1903.06133.

2. Optimal Integration of Supervised Tensor Decomposition and Ensemble Learning for In Situ Quality Evaluation in Friction Stir Blind Riveting

3. Some special topics in multivariate image analysis

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