Tensor Networks
59 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Tensor Networks
Libraries
Use these libraries to find Tensor Networks models and implementationsLatest papers
Multi-layered tensor networks for image classification
raghavian/mltn
•
•
The recently introduced locally orderless tensor network (LoTeNet) for supervised image classification uses matrix product state (MPS) operations on grids of transformed image patches.
Multi-Graph Tensor Networks
The irregular and multi-modal nature of numerous modern data sources poses serious challenges for traditional deep learning algorithms.
Locally orderless tensor networks for classifying two- and three-dimensional medical images
raghavian/mltn
•
•
The proposed locally orderless tensor network (LoTeNet) is compared with relevant methods on three datasets.
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.
Deep convolutional tensor network
philip-bl/dctn
•
•
Also, DCTN of any depth performs badly on CIFAR10 due to overfitting.
Quantum-Classical Machine learning by Hybrid Tensor Networks
dingliu0305/Hybrid-Tensor-Network
•
•
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.
Tensor Networks for Medical Image Classification
raghavian/loTeNet_pytorch
•
•
MIDL 2019
With the increasing adoption of machine learning tools like neural networks across several domains, interesting connections and comparisons to concepts from other domains are coming to light.
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.
Supervised Learning for Non-Sequential Data: A Canonical Polyadic Decomposition Approach
KritonKonstantinidis/CPD_Supervised_Learning
•
•
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).
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.
