Holographic Recurrent Networks
Abstract
Holographic Recurrent Networks (HRNs) are recurrent networks which incorporate associative memory techniques for storing se(cid:173) quential structure. HRNs can be easily and quickly trained using gradient descent techniques to generate sequences of discrete out(cid:173) puts and trajectories through continuous spaee. The performance of HRNs is found to be superior to that of ordinary recurrent net(cid:173) works on these sequence generation tasks.
Cite
Text
Plate. "Holographic Recurrent Networks." Neural Information Processing Systems, 1992.Markdown
[Plate. "Holographic Recurrent Networks." Neural Information Processing Systems, 1992.](https://mlanthology.org/neurips/1992/plate1992neurips-holographic/)BibTeX
@inproceedings{plate1992neurips-holographic,
title = {{Holographic Recurrent Networks}},
author = {Plate, Tony A.},
booktitle = {Neural Information Processing Systems},
year = {1992},
pages = {34-41},
url = {https://mlanthology.org/neurips/1992/plate1992neurips-holographic/}
}