README
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GoSLIDE
GoSLIDE is a Go module implementing the Sub-Linear Deep learning Engine (SLIDE) algorithm introduced by Chen et al, 2019.
This is a work-in-progress port from the original codebase, written in C++.
Once we have a first complete port, we expect to focus on cleaning, refactoring, testing, and maybe even re-engineering some components. Eventually, we will evaluate further expansions, and a possible integration or combination with the spago library.
License
GoSLIDE is licensed under the BSD 2-Clause "Simplified" License. For information about the authors, please refer to the file AUTHORS.md.
This project is strongly based on HashingDeepLearning, which is licensed under the MIT License.
Documentation
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There is no documentation for this package.
Source Files
Directories
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| Path | Synopsis |
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xcrepo
I/O support for The Extreme Classification Repository Multi-label Datasets sparse format.
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I/O support for The Extreme Classification Repository Multi-label Datasets sparse format. |
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Implementation of Densified Winner Take All (WTA) hash Algorithm from the paper: Densified Winner Take All (WTA) Hashing for Sparse Datasets Beidi Chen, Anshumali Shrivastava http://auai.org/uai2018/proceedings/papers/321.pdf
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Implementation of Densified Winner Take All (WTA) hash Algorithm from the paper: Densified Winner Take All (WTA) Hashing for Sparse Datasets Beidi Chen, Anshumali Shrivastava http://auai.org/uai2018/proceedings/papers/321.pdf |
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Implementation of Winner Take All (WTA) hash Algorithm from the paper: The Power of Comparative Reasoning Jay Yagnik, Dennis Strelow, David A. Ross, Ruei-sung Lin https://www.cs.toronto.edu/~dross/YagnikStrelowRossLin_ICCV2011.pdf
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Implementation of Winner Take All (WTA) hash Algorithm from the paper: The Power of Comparative Reasoning Jay Yagnik, Dennis Strelow, David A. Ross, Ruei-sung Lin https://www.cs.toronto.edu/~dross/YagnikStrelowRossLin_ICCV2011.pdf |
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