Hallam, A;
Grant, E;
Stojevic, V;
Severini, S;
Green, AG;
(2018)
Compact neural networks based on the multiscale entanglement renormalization Ansatz.
In:
British Machine Vision Conference 2018, BMVC 2018.
BMVC: Newcastle-upon-Tyne, UK.
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Abstract
This paper demonstrates a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement Renormalization Ansatz (MERA). We employ MERA as a replacement for the fully connected layers in a convolutional neural network and test this implementation on the CIFAR-10 and CIFAR-100 datasets. The proposed method outperforms factorization using tensor trains, providing greater compression for the same level of accuracy and greater accuracy for the same level of compression. We demonstrate MERA layers with 14000 times fewer parameters and a reduction in accuracy of less than 1% compared to the equivalent fully connected layers, scaling like O(N).
| Type: | Proceedings paper |
|---|---|
| Title: | Compact neural networks based on the multiscale entanglement renormalization Ansatz |
| Event: | BMVC 2018 - 29TH British Machine Vision Conference |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://bmvc2018.org/programmedetail.html |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > London Centre for Nanotechnology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10082844 |
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