Multi-level optimization of the canonical polyadic tensor decomposition at large-scale: Application to the stratification of social networks through deflation-Reference-Cited by-同舟云学术

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Multi-level optimization of the canonical polyadic tensor decomposition at large-scale: Application to the stratification of social networks through deflation

Published:2023-02 Issue: Volume:112 Page:102142
ISSN:0306-4379
Container-title:Information Systems
language:en
Short-container-title:Information Systems

Author:

Gillet Annabelle,Leclercq Éric,Cullot Nadine

Publisher

Elsevier BV

Subject

Hardware and Architecture,Information Systems,Software

Reference37 articles.

1. Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-Way Data Analysis and Blind Source Separation;Cichocki,2009

2. M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al., Tensorflow: A system for large-scale machine learning, in: 12th USENIX Symposium on Operating Systems Design and Implementation, 2016, pp. 265–283.

3. PyTorch: An imperative style, high-performance deep learning library;Paszke,2019

4. Theano: A Python framework for fast computation of mathematical expressions;Al-Rfou,2016

5. Tensorly: Tensor learning in python;Kossaifi;J. Mach. Learn. Res.,2019

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1. Charging Time Prediction of Electric Vehicle Charging Pile via Momentum-incorporated Non-negative Latent-factorization-of-tensors with Swish-regularization;2023 IEEE 6th International Conference on Pattern Recognition and Artificial Intelligence (PRAI);2023-08-18

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