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Residual Tensor Train: a Flexible and Efficient Approach for Learning Multiple Multilinear Correlations
[article]
2021
arXiv
pre-print
Tensor Train (TT) approach has been successfully applied in the modelling of the multilinear interaction of features. Nevertheless, the existing models lack flexibility and generalizability, as they only model a single type of high-order correlation. In practice, multiple multilinear correlations may exist within the features. In this paper, we present a novel Residual Tensor Train (ResTT) which integrates the merits of TT and residual structure to capture the multilinear feature correlations,
arXiv:2108.08659v1
fatcat:pgpdgp6tv5bvnhhagjdfdqlxtq