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Found 5 papers, 2 papers with code
Graph Tensor Networks: An Intuitive Framework for Designing Large-Scale Neural Learning Systems on Multiple Domains
Despite the omnipresence of tensors and tensor operations in modern deep learning, the use of tensor mathematics to formally design and describe neural networks is still under-explored within the deep learning community.
Graph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Multi-Way Financial Modelling
This represents a challenge for modern machine learning models, as the number of model parameters needed to process such data grows exponentially with the data dimensions; an effect known as the Curse-of-Dimensionality.
Tensor Networks for Multi-Modal Non-Euclidean Data
Modern data sources are typically of large scale and multi-modal natures, and acquired on irregular domains, which poses serious challenges to traditional deep learning models.
Multi-Graph Tensor Networks
The irregular and multi-modal nature of numerous modern data sources poses serious challenges for traditional deep learning algorithms.
Supervised Learning for Non-Sequential Data: A Canonical Polyadic Decomposition Approach
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).
