# Cognitive modeling and learning with sparse binary hypervectors

arXiv: https://arxiv.org/abs/2310.18316 · HTML: https://mlsanity.com/paper/2310.18316/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 16 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [A comparison of Vector Symbolic Architectures](https://mlsanity.com/paper/2001.11797/) (2020)
- [Commentaries on "Learning Sensorimotor Control with Neuromorphic Sensors: Toward Hyperdimensional Active Perception" [Science Robotics Vol. 4 Issue 30 (2019) 1-10](https://mlsanity.com/paper/2003.11458/) (2020)

## Built on by (most cited first, 1 of 1)

- [Autonomous Learning with High-Dimensional Computing Architecture Similar to von Neumann's](https://mlsanity.com/paper/2503.23608/) (2025)

## Abstract (arXiv)

Following the general theoretical framework of VSA (Vector Symbolic Architecture), a cognitive model with the use of sparse binary hypervectors is proposed. In addition, learning algorithms are introduced to bootstrap the model from incoming data stream, with much improved transparency and efficiency. Mimicking human cognitive process, the training can be performed online while inference is in session. Word-level embedding is re-visited with such hypervectors, and further applications in the field of NLP (Natural Language Processing) are explored.