License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.SoCG.2019.1
URN: urn:nbn:de:0030-drops-104055
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2019/10405/
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Dasgupta, Sanjoy

A Geometric Data Structure from Neuroscience (Invited Talk)

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LIPIcs-SoCG-2019-1.pdf (0.2 MB)

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).

BibTeX - Entry

@InProceedings{dasgupta:LIPIcs:2019:10405,
  author =	{Sanjoy Dasgupta},
  title =	{{A Geometric Data Structure from Neuroscience (Invited Talk)}},
  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 =	{Gill Barequet and Yusu Wang},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2019/10405},
  URN =		{urn:nbn:de:0030-drops-104055},
  doi =		{10.4230/LIPIcs.SoCG.2019.1},
  annote =	{Keywords: Geometric data structure, algorithm design, neuroscience}
}

Keywords: Geometric data structure, algorithm design, neuroscience
Collection: 35th International Symposium on Computational Geometry (SoCG 2019)
Issue Date: 2019
Date of publication: 11.06.2019

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