Compact Neural Networks based on the Multiscale Entanglement Renormalization Ansatz
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).
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Differentiable Programming of Isometric Tensor Networks
Differentiable programming is a new programming paradigm which enables large scale optimization through automatic calculation of gradients also known as auto-differentiation. This concept emerges from deep learning, and …
Tensor NetworksEntanglement-Embedded Recurrent Network Architecture: Tensorized Latent State Propagation and Chaos Forecasting
Chaotic time series forecasting has been far less understood despite its tremendous potential in theory and real-world applications. Traditional statistical/ML methods are inefficient to capture chaos in nonlinear dynami…
Tensor DecompositionTime SeriesTime Series AnalysisTime Series ForecastingQuantum Phase Recognition using Quantum Tensor Networks
Machine learning (ML) has recently facilitated many advances in solving problems related to many-body physical systems. Given the intrinsic quantum nature of these problems, it is natural to speculate that quantum-enhanc…
image-classificationImage ClassificationQuantum Machine LearningTensor NetworksA Multi-Scale Tensor Network Architecture for Classification and Regression
We present an algorithm for supervised learning using tensor networks, employing a step of preprocessing the data by coarse-graining through a sequence of wavelet transformations. We represent these transformations as a …
ClassificationGeneral ClassificationregressionTensor Networks+2Machine Learning by Unitary Tensor Network of Hierarchical Tree Structure
The resemblance between the methods used in quantum-many body physics and in machine learning has drawn considerable attention. In particular, tensor networks (TNs) and deep learning architectures bear striking similarit…
BIG-bench Machine LearningTensor Networks