ML Anthology

Learning Temporally Persistent Hierarchical Representations

Suzanna Becker
NeurIPS 1996 pp. 824-830
/neurips/1996/becker1996neurips-learning/

Abstract

A biologically motivated model of cortical self-organization is pro(cid:173) posed. Context is combined with bottom-up information via a maximum likelihood cost function. Clusters of one or more units are modulated by a common contextual gating Signal; they thereby organize themselves into mutually supportive predictors of abstract contextual features. The model was tested in its ability to discover viewpoint-invariant classes on a set of real image sequences of cen(cid:173) tered, gradually rotating faces. It performed considerably better than supervised back-propagation at generalizing to novel views from a small number of training examples.

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Cite

Text

Becker. "Learning Temporally Persistent Hierarchical Representations." Neural Information Processing Systems, 1996.

Markdown

[Becker. "Learning Temporally Persistent Hierarchical Representations." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/becker1996neurips-learning/)

BibTeX

@inproceedings{becker1996neurips-learning,
  title     = {{Learning Temporally Persistent Hierarchical Representations}},
  author    = {Becker, Suzanna},
  booktitle = {Neural Information Processing Systems},
  year      = {1996},
  pages     = {824-830},
  url       = {https://mlanthology.org/neurips/1996/becker1996neurips-learning/}
}