Cannikin's Law in Tensor Modeling: A Rank Study for Entanglement and Separability in Tensor Complexity and Model Capacity
Quantum Physics
2022-04-19 v1 Machine Learning
Numerical Analysis
Numerical Analysis
Abstract
This study clarifies the proper criteria to assess the modeling capacity of a general tensor model. The work analyze the problem based on the study of tensor ranks, which is not a well-defined quantity for higher order tensors. To process, the author introduces the separability issue to discuss the Cannikin's law of tensor modeling. Interestingly, a connection between entanglement studied in information theory and tensor analysis is established, shedding new light on the theoretical understanding for modeling capacity problems.
Keywords
Cite
@article{arxiv.2204.07760,
title = {Cannikin's Law in Tensor Modeling: A Rank Study for Entanglement and Separability in Tensor Complexity and Model Capacity},
author = {Tong Yang},
journal= {arXiv preprint arXiv:2204.07760},
year = {2022}
}
Comments
7 pages