Supervised learning with tensor networks

E. M. Stoudenmire, David J. Schwab

Research output: Contribution to journalArticlepeer-review

76 Scopus citations

Abstract

Tensor networks are approximations of high-order tensors which are efficient to work with and have been very successful for physics and mathematics applications. We demonstrate how algorithms for optimizing tensor networks can be adapted to supervised learning tasks by using matrix product states (tensor trains) to parameterize non-linear kernel learning models. For the MNIST data set we obtain less than 1% test set classification error. We discuss an interpretation of the additional structure imparted by the tensor network to the learned model.

Original languageEnglish (US)
Pages (from-to)4806-4814
Number of pages9
JournalAdvances in Neural Information Processing Systems
StatePublished - Jan 1 2016

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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