# Tensor Products and Hyperdimensional Computing

arXiv: https://arxiv.org/abs/2305.10572 · HTML: https://mlsanity.com/paper/2305.10572/

## 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 17 May 2023.

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

- [Graph Embeddings via Tensor Products and Approximately Orthonormal Codes](https://mlsanity.com/paper/2208.10917/) (2022)

## Abstract (arXiv)

Following up on a previous analysis of graph embeddings, we generalize and expand some results to the general setting of vector symbolic architectures (VSA) and hyperdimensional computing (HDC). Importantly, we explore the mathematical relationship between superposition, orthogonality, and tensor product. We establish the tensor product representation as the central representation, with a suite of unique properties. These include it being the most general and expressive representation, as well as being the most compressed representation that has errorrless unbinding and detection.