# Differentiable Neural Computers with Memory Demon

arXiv: https://arxiv.org/abs/2211.02987 · HTML: https://mlsanity.com/paper/2211.02987/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 5 Nov 2022.

## Builds on (most cited first, 3 of 3)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes](https://mlsanity.com/paper/1610.09027/) (2016)
- [Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control](https://mlsanity.com/paper/1904.10278/) (2019)

## Abstract (arXiv)

A Differentiable Neural Computer (DNC) is a neural network with an external memory which allows for iterative content modification via read, write and delete operations. We show that information theoretic properties of the memory contents play an important role in the performance of such architectures. We introduce a novel concept of memory demon to DNC architectures which modifies the memory contents implicitly via additive input encoding. The goal of the memory demon is to maximize the expected sum of mutual information of the consecutive external memory contents.