Compact Neural Networks based on the Multiscale Entanglement Renormalization Ansatz

ICLR 2018 Andrew HallamEdward GrantVid StojevicSimone SeveriniAndrew G. Green

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... (read more)

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