Tensor Networks
59 papers with code • 0 benchmarks • 0 datasets
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Libraries
Use these libraries to find Tensor Networks models and implementationsMost implemented papers
Tensor-based algorithms for image classification
The interest in machine learning with tensor networks has been growing rapidly in recent years.
Expressive power of tensor-network factorizations for probabilistic modeling
Inspired by these developments, and the natural correspondence between tensor networks and probabilistic graphical models, we provide a rigorous analysis of the expressive power of various tensor-network factorizations of discrete multivariate probability distributions.
Algorithms for Tensor Network Contraction Ordering
We compare the obtained contraction sequences and identify signs of highly non-local optimization, with the more sophisticated algorithms sacrificing run-time early in the contraction for better overall performance.
Supervised Learning for Non-Sequential Data: A Canonical Polyadic Decomposition Approach
KritonKonstantinidis/CPD_Supervised_Learning
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However, both TT and other Tensor Networks (TNs), such as Tensor Ring and Hierarchical Tucker, are sensitive to the ordering of their indices (and hence to the features).
Tensor Networks for Probabilistic Sequence Modeling
Tensor networks are a powerful modeling framework developed for computational many-body physics, which have only recently been applied within machine learning.
Quantum-Classical Machine learning by Hybrid Tensor Networks
dingliu0305/Hybrid-Tensor-Network
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In this work, we propose the quantum-classical hybrid tensor networks (HTN) which combine tensor networks with classical neural networks in a uniform deep learning framework to overcome the limitations of regular tensor networks in machine learning.
Deep convolutional tensor network
philip-bl/dctn
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Also, DCTN of any depth performs badly on CIFAR10 due to overfitting.
T-Basis: a Compact Representation for Neural Networks
Each of the tensors in the set is modeled using Tensor Rings, though the concept applies to other Tensor Networks.
Locally orderless tensor networks for classifying two- and three-dimensional medical images
raghavian/mltn
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The proposed locally orderless tensor network (LoTeNet) is compared with relevant methods on three datasets.
Multi-Graph Tensor Networks
The irregular and multi-modal nature of numerous modern data sources poses serious challenges for traditional deep learning algorithms.
