ML Anthology

Constant-Time Loading of Shallow 1-Dimensional Networks

Stephen Judd
NeurIPS 1991 pp. 863-870
/neurips/1991/judd1991neurips-constanttime/

Abstract

The complexity of learning in shallow I-Dimensional neural networks has been shown elsewhere to be linear in the size of the network. However, when the network has a huge number of units (as cortex has) even linear time might be unacceptable. Furthermore, the algorithm that was given to achieve this time was based on a single serial processor and was biologically implausible. In this work we consider the more natural parallel model of processing and demonstrate an expected-time complexity that is constant (i.e. dependent of the size of the network). This holds even when inter-node communication channels are short and local, thus adhering to more bio(cid:173) logical and VLSI constraints.

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Cite

Text

Judd. "Constant-Time Loading of Shallow 1-Dimensional Networks." Neural Information Processing Systems, 1991.

Markdown

[Judd. "Constant-Time Loading of Shallow 1-Dimensional Networks." Neural Information Processing Systems, 1991.](https://mlanthology.org/neurips/1991/judd1991neurips-constanttime/)

BibTeX

@inproceedings{judd1991neurips-constanttime,
  title     = {{Constant-Time Loading of Shallow 1-Dimensional Networks}},
  author    = {Judd, Stephen},
  booktitle = {Neural Information Processing Systems},
  year      = {1991},
  pages     = {863-870},
  url       = {https://mlanthology.org/neurips/1991/judd1991neurips-constanttime/}
}