skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: 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:
 [1]
  1. 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}
}