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There has been growing interest in extending traditional vector-based machine learning techniques to their tensor forms.
Tensor rank is NP-complete
J. Håstad · 1990
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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A comparison of methods for multiclass support vector machines
Chih-Wei Hsu and Chih-Jen Lin · 2002
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Supervised tensor learning
Dacheng Tao, Xuelong Li, Weiming Hu, Stephen Maybank, and Xindong Wu · 2005
Earlier work this paper cites.
Multitraining support vector machine for image retrieval
Jing Li, Nigel Allinson, Dacheng Tao, and Xuelong Li · 2006
Earlier work this paper cites.
Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval
Dacheng Tao, Xiaoou Tang, Xuelong Li, and Xindong Wu · 2006
Earlier work this paper cites.
Supervised tensor learning
Dacheng Tao, Xuelong Li, Xindong Wu, Weiming Hu, and Stephen J. Maybank · 2007
Earlier work this paper cites.
Multilinear discriminant analysis for face recognition
Shuicheng Yan, Dong Xu, Qiang Yang, Lei Zhang, Xiaoou Tang, and Hong-Jiang Zhang · 2007
Cited alongside, same era.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Cited alongside, same era.
Support Tucker Machines
Irene Kotsia and Ioannis Patras · 2011
Cited alongside, same era.
Tensor-train decomposition
Ivan V Oseledets · 2011
Cited alongside, same era.
The density-matrix renormalization group in the age of matrix product states
Ulrich Schollwöck · 2011
Cited alongside, same era.
Higher rank support tensor machines for visual recognition
Irene Kotsia, Weiwei Guo, and Ioannis Patras · 2012
Cited alongside, same era.
Speeding-up convolutional neural networks using fine-tuned cp-decomposition
Vadim Lebedev, Yaroslav Ganin, Maksim Rakhuba, Ivan Oseledets, and Victor Lempitsky · 2014
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A practical introduction to tensor networks: Matrix product states and projected entangled pair states
Román Orús · 2014
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Learning with tensors: a framework based on convex optimization and spectral regularization
Marco Signoretto, Quoc Tran Dinh, Lieven De Lathauwer, and Johan AK Suykens · 2014
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Tensorizing neural networks
Alexander Novikov, Dmitrii Podoprikhin, Anton Osokin, and Dmitry P Vetrov · 2015
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Supervised learning with quantum-inspired tensor networks
E Miles Stoudenmire and David J Schwab · 2016
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The nature of statistical learning theory
Vladimir Vapnik · 2013
Cited alongside, same era.
Alexander Novikov, Mikhail Trofimov, and Ivan Oseledets · 2016
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Parallelized tensor train learning of polynomial classifiers
Zhongming Chen, Kim Batselier, Johan AK Suykens, and Ngai Wong · 2017
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