Computing Time Lower Bounds for Recurrent Sigmoidal Neural Networks
Abstract
Recurrent neural networks of analog units are computers for real(cid:173) valued functions. We study the time complexity of real computa(cid:173) tion in general recurrent neural networks. These have sigmoidal, linear, and product units of unlimited order as nodes and no re(cid:173) strictions on the weights. For networks operating in discrete time, we exhibit a family of functions with arbitrarily high complexity, and we derive almost tight bounds on the time required to compute these functions. Thus, evidence is given of the computational lim(cid:173) itations that time-bounded analog recurrent neural networks are subject to.
Cite
Text
Schmitt. "Computing Time Lower Bounds for Recurrent Sigmoidal Neural Networks." Neural Information Processing Systems, 2001.Markdown
[Schmitt. "Computing Time Lower Bounds for Recurrent Sigmoidal Neural Networks." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/schmitt2001neurips-computing/)BibTeX
@inproceedings{schmitt2001neurips-computing,
title = {{Computing Time Lower Bounds for Recurrent Sigmoidal Neural Networks}},
author = {Schmitt, M.},
booktitle = {Neural Information Processing Systems},
year = {2001},
pages = {503-510},
url = {https://mlanthology.org/neurips/2001/schmitt2001neurips-computing/}
}