torchNALU
Code in this repository provides a PyTorch implementation of the
Neural Arithmetic Logic Units
paper from Deepmind. The results produced align with those described
within the paper, demonstrating the effectiveness of the NALU/NAC
architectures.
Experiments:
To reproduce the results for a static task, run:
python3 network.py
The graphs below show the MSE normalised with respect to the results obtained from a random MLP network such that 100.0 is equivalent to random, 0.0 is perfect accuracy, and >100.0 is worse than a randomly initialised model.
References:
@misc{trask2018neural,
title={Neural Arithmetic Logic Units},
author={Andrew Trask and Felix Hill and Scott Reed and Jack Rae and Chris Dyer and Phil Blunsom},
year={2018},
eprint={1808.00508},
archivePrefix={arXiv},
primaryClass={cs.NE}
}