Andreas Stöckel. Discrete function bases and convolutional neural networks. arXiv preprint arXiv:2103.05609, 2021. URL: https://arxiv.org/abs/2103.05609.
@article{stoeckel2021b,
title={Discrete Function Bases and Convolutional Neural Networks},
author={Andreas St\"ockel},
journal={arXiv preprint arXiv:2103.05609},
year={2021},
url={https://arxiv.org/abs/2103.05609},
pdf={/files/publications/stoeckel.2021b.pdf},
abstract={We discuss the notion of "discrete function bases" with a particular focus on the discrete basis derived from the Legendre Delay Network (LDN). We characterize the performance of these bases in a delay computation task, and as fixed temporal convolutions in neural networks. Networks using fixed temporal convolutions are conceptually simple and yield state-of-the-art results in tasks such as psMNIST.}
}