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Invited Talk

DOI: 10.4230/LIPIcs.SoCG.2019.1

A Geometric Data Structure from Neuroscience (Invited Talk)

Authors: Sanjoy Dasgupta

Published in: LIPIcs, Volume 129, 35th International Symposium on Computational Geometry (SoCG 2019)

Abstract

An intriguing geometric primitive, "expand-and-sparsify", has been found in the olfactory system of the fly and several other organisms. It maps an input vector to a much higher-dimensional sparse representation, using a random linear transformation followed by winner-take-all thresholding. I will show that this representation has a variety of formal properties, such as locality preservation, that make it an attractive data structure for algorithms and machine learning. In particular, mimicking the fly’s circuitry yields algorithms for similarity search and for novelty detection that have provable guarantees as well as having practical performance that is competitive with state-of-the-art methods. This talk is based on work with Saket Navlakha (Salk Institute), Chuck Stevens (Salk Institute), and Chris Tosh (Columbia).

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Sanjoy Dasgupta. A Geometric Data Structure from Neuroscience (Invited Talk). In 35th International Symposium on Computational Geometry (SoCG 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 129, p. 1:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{dasgupta:LIPIcs.SoCG.2019.1,
  author =	{Dasgupta, Sanjoy},
  title =	{{A Geometric Data Structure from Neuroscience}},
  booktitle =	{35th International Symposium on Computational Geometry (SoCG 2019)},
  pages =	{1:1--1:1},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-104-7},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{129},
  editor =	{Barequet, Gill and Wang, Yusu},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2019.1},
  URN =		{urn:nbn:de:0030-drops-104055},
  doi =		{10.4230/LIPIcs.SoCG.2019.1},
  annote =	{Keywords: Geometric data structure, algorithm design, neuroscience}
}

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A Geometric Data Structure from Neuroscience (Invited Talk)

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