Emergence of Global Structure from Local Associations
Abstract
A variant of the encoder architecture, where units at the input and out(cid:173) put layers represent nodes on a graph. is applied to the task of mapping locations to sets of neighboring locations. The degree to which the re(cid:173) suIting internal (i.e. hidden unit) representations reflect global proper(cid:173) ties of the environment depends upon several parameters of the learning procedure. Architectural bottlenecks. noise. and incremental learning of landmarks are shown to be important factors in maintaining topograph(cid:173) ic relationships at a global scale.
Cite
Text
Ghiselli-Crippa and Munro. "Emergence of Global Structure from Local Associations." Neural Information Processing Systems, 1993.Markdown
[Ghiselli-Crippa and Munro. "Emergence of Global Structure from Local Associations." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/ghisellicrippa1993neurips-emergence/)BibTeX
@inproceedings{ghisellicrippa1993neurips-emergence,
title = {{Emergence of Global Structure from Local Associations}},
author = {Ghiselli-Crippa, Thea B. and Munro, Paul W.},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {1101-1108},
url = {https://mlanthology.org/neurips/1993/ghisellicrippa1993neurips-emergence/}
}