Sparse sign-consistent Johnson-Lindenstrauss matrices: Compression with neuroscience-based constraints
Johnson-Lindenstrauss (JL) matrices implemented by sparse random synaptic connections are thought to be a prime candidate for how convergent pathways in the brain compress information. However, to date, there is no complete mathematical support for such implementations given the constraints of real...
Main Authors: | Allen-Zhu, Zeyuan (Contributor), Gelashvili, Rati (Contributor), Micali, Silvio (Contributor), Shavit, Nir N. (Contributor) |
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Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor) |
Format: | Article |
Language: | English |
Published: |
National Academy of Sciences (U.S.),
2015-06-09T15:35:55Z.
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Subjects: | |
Online Access: | Get fulltext |
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