# Entropic Associative Memory for Manuscript Symbols

arXiv: https://arxiv.org/abs/2202.08413 · HTML: https://mlsanity.com/paper/2202.08413/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Hopfield Networks is All You Need](https://mlsanity.com/paper/2008.02217/) (2020)
- [Dense Associative Memory for Pattern Recognition](https://mlsanity.com/paper/1606.01164/) (2016)
- [EMNIST: an extension of MNIST to handwritten letters](https://mlsanity.com/paper/1702.05373/) (2017)
- [Associative Long Short-Term Memory](https://mlsanity.com/paper/1602.03032/) (2016)
- [An Entropic Associative Memory](https://mlsanity.com/paper/2009.13058/) (2020)
- [The Mode of Computing](https://mlsanity.com/paper/1903.10559/) (2019)
- [Entropy, Computing and Rationality](https://mlsanity.com/paper/2009.10224/) (2020)

## Built on by (most cited first, 2 of 2)

- [Entropic associative memory for real world images](https://mlsanity.com/paper/2405.12500/) (2024)
- [Entropic Hetero-Associative Memory](https://mlsanity.com/paper/2411.02438/) (2024)

## Abstract (arXiv)

Manuscript symbols can be stored, recognized and retrieved from an entropic digital memory that is associative and distributed but yet declarative; memory retrieval is a constructive operation, memory cues to objects not contained in the memory are rejected directly without search, and memory operations can be performed through parallel computations. Manuscript symbols, both letters and numerals, are represented in Associative Memory Registers that have an associated entropy. The memory recognition operation obeys an entropy trade-off between precision and recall, and the entropy level impacts on the quality of the objects recovered through the memory retrieval operation. The present proposal is contrasted in several dimensions with neural networks models of associative memory. We discuss the operational characteristics of the entropic associative memory for retrieving objects with both complete and incomplete information, such as severe occlusions. The experiments reported in this paper add evidence on the potential of this framework for developing practical applications and computational models of natural memory.