On the Non-Existence of a Universal Learning Algorithm for Recurrent Neural Networks
Abstract
We prove that the so called "loading problem" for (recurrent) neural net(cid:173) works is unsolvable. This extends several results which already demon(cid:173) strated that training and related design problems for neural networks are (at least) NP-complete. Our result also implies that it is impossible to find or to formulate a universal training algorithm, which for any neu(cid:173) ral network architecture could determine a correct set of weights. For the simple proof of this, we will just show that the loading problem is equivalent to "Hilbert's tenth problem" which is known to be unsolvable.
Cite
Text
Wiklicky. "On the Non-Existence of a Universal Learning Algorithm for Recurrent Neural Networks." Neural Information Processing Systems, 1993.Markdown
[Wiklicky. "On the Non-Existence of a Universal Learning Algorithm for Recurrent Neural Networks." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/wiklicky1993neurips-nonexistence/)BibTeX
@inproceedings{wiklicky1993neurips-nonexistence,
title = {{On the Non-Existence of a Universal Learning Algorithm for Recurrent Neural Networks}},
author = {Wiklicky, Herbert},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {431-436},
url = {https://mlanthology.org/neurips/1993/wiklicky1993neurips-nonexistence/}
}