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Tensor Train decomposition is used across many branches of machine learning.
Finitely correlated states on quantum spin chains
M. Fannes, B. Nachtergaele, and R.F. Werner · 1992
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DMRG+QTT approach to computation of the ground state for the molecular Schrödinger operator
B. N. Khoromskij and I. V. Oseledets · 2010
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evoMPS: An implementation of the time dependent variational principle for matrix product states , 2011
Ashley Milsted · 2011
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Tensor-Train decomposition
I. V. Oseledets · 2011
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TT-Toolbox: Matlab implementation of Tensor Train decomposition , 2011
Ivan Oseledets, Sergey V. Dolgov, Alexey Boyko, Dmitry Savostyanov, Alexander Novikov, and Thomas Mach · 2011
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A spectral algorithm for latent dirichlet allocation
A. Anandkumar, D. P. Foster, D. Hsu, S. M. Kakade, and L. Yi-kai · 2012
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ttpy: Python implementation of the TT-Toolbox , 2012
Ivan Oseledets · 2012
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A literature survey of low-rank tensor approximation techniques
Lars Grasedyck, Daniel Kressner, and Christine Tobler · 2013
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Discovering hidden variables in noisy-or networks using quartet tests
Y. Jernite, Y. Halpern, and D. Sontag · 2013
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itensor: An implementation of the time dependent variational principle for matrix product states , 2013
E. Miles Stoudenmire Matthew Fishman and Steven R. White · 2013
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libtt library , 2013
Sergey Matveev · 2013
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mps: Library for the simulation of one-dimensional quantum many-body systems , 2013
Juan José García Ripoll · 2013
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Hierarchical tensor decomposition of latent tree graphical models
L. Song, M. Ishteva, A. Parikh, E. Xing, and H. Park · 2013
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Alternating minimal energy methods for linear systems in higher dimensions
Sergey V Dolgov and Dmitry V Savostyanov · 2014
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Beating the perils of non-convexity: Guaranteed training of neural networks using tensor methods
M. Janzamin, H. Sedghi, and A. Anandkumar · 2015
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Speeding-up convolutional neural networks using fine-tuned cp-decomposition
V. Lebedev, Y. Ganin, M. Rakhuba, I. Oseledets, and V. Lempitsky · 2015
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Tensorizing neural networks
A. Novikov, D. Podoprikhin, A. Osokin, and D. Vetrov · 2015
Tensor methods and recommender systems
E. Frolov and I. Oseledets · 2017
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Expressive power of recurrent neural networks
V. Khrulkov, A. Novikov, and I. Oseledets · 2017
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mpnum: A matrix product representation library for Python
Daniel Suess and Milan Holzäpfel · 2017
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Tensor-train recurrent neural networks for video classification
Yinchong Yang, Denis Krompass, and Volker Tresp · 2017
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Long-term forecasting using tensor-train rnns
R. Yu, S. Zheng, A. Anandkumar, and Y. Yue · 2017
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tntorch: Tensor Network Learning with PyTorch , 2018
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Convolutional rectifier networks as generalized tensor decompositions
N. Cohen and A. Shashua · 2016
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On the expressive power of deep learning: A tensor analysis
N. Cohen, O. Sharir, and A. Shashua · 2016
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Riemannian optimization for solving high-dimensional problems with low-rank tensor structure
M. Steinlechner · 2016
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Supervised learning with tensor networks
Edwin Stoudenmire and David J Schwab · 2016
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mpys: Library for MPS computations in Python , 2017
Alvarorga · 2017
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Rafael Ballester-Ripoll · 2018
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scikit_tt: Tensor Train Toolbox , 2018
Patrick Gelß, Stefan Klus, Martin Scherer, and Feliks Nüske · 2018
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Open source matrix product states: Opening ways to simulate entangled many-body quantum systems in one dimension
Daniel Jaschke, Michael L Wall, and Lincoln D Carr · 2018
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Tensor numerical methods in scientific computing , volume 19
Boris N Khoromskij · 2018
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Tensorly: Tensor learning in python
Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, and Maja Pantic · 2019
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Low-rank riemannian eigensolver for high-dimensional hamiltonians
Maxim Rakhuba, Alexander Novikov, and Ivan Oseledets · 2019
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