Generative Modeling for Machine Learning on the D-Wave
Abstract
These are slides on Generative Modeling for Machine Learning on the D-Wave. The following topics are detailed: generative models; Boltzmann machines: a generative model; restricted Boltzmann machines; learning parameters: RBM training; practical ways to train RBM; D-Wave as a Boltzmann sampler; mapping RBM onto the D-Wave; Chimera restricted RBM; mapping binary RBM to Ising model; experiments; data; D-Wave effective temperature, parameters noise, etc.; experiments: contrastive divergence (CD) 1 step; after 50 steps of CD; after 100 steps of CD; D-Wave (experiments 1, 2, 3); D-Wave observations.
- Authors:
-
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Information Sciences Group
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1332219
- Report Number(s):
- LA-UR-16-28813
- DOE Contract Number:
- AC52-06NA25396
- Resource Type:
- Technical Report
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; Computer Science; Information Science; d-wave; machine learning; restricted boltzmann machine
Citation Formats
Thulasidasan, Sunil. Generative Modeling for Machine Learning on the D-Wave. United States: N. p., 2016.
Web. doi:10.2172/1332219.
Thulasidasan, Sunil. Generative Modeling for Machine Learning on the D-Wave. United States. https://doi.org/10.2172/1332219
Thulasidasan, Sunil. 2016.
"Generative Modeling for Machine Learning on the D-Wave". United States. https://doi.org/10.2172/1332219. https://www.osti.gov/servlets/purl/1332219.
@article{osti_1332219,
title = {Generative Modeling for Machine Learning on the D-Wave},
author = {Thulasidasan, Sunil},
abstractNote = {These are slides on Generative Modeling for Machine Learning on the D-Wave. The following topics are detailed: generative models; Boltzmann machines: a generative model; restricted Boltzmann machines; learning parameters: RBM training; practical ways to train RBM; D-Wave as a Boltzmann sampler; mapping RBM onto the D-Wave; Chimera restricted RBM; mapping binary RBM to Ising model; experiments; data; D-Wave effective temperature, parameters noise, etc.; experiments: contrastive divergence (CD) 1 step; after 50 steps of CD; after 100 steps of CD; D-Wave (experiments 1, 2, 3); D-Wave observations.},
doi = {10.2172/1332219},
url = {https://www.osti.gov/biblio/1332219},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Nov 15 00:00:00 EST 2016},
month = {Tue Nov 15 00:00:00 EST 2016}
}
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