# Cannikin's Law in Tensor Modeling: A Rank Study for Entanglement and Separability in Tensor Complexity and Model Capacity

arXiv: https://arxiv.org/abs/2204.07760 · HTML: https://mlsanity.com/paper/2204.07760/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 3 references that are in the map.
- First posted on arXiv 16 Apr 2022.

## Builds on (most cited first, 1 of 1)

- [Entanglement Entropy of Target Functions for Image Classification and Convolutional Neural Network](https://mlsanity.com/paper/1710.05520/) (2017)

## Abstract (arXiv)

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.