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Statistics > Machine Learning

arXiv:1312.5258 (stat)
[Submitted on 18 Dec 2013 (v1), last revised 24 Oct 2014 (this version, v2)]

Title:On the Challenges of Physical Implementations of RBMs

Authors:Vincent Dumoulin, Ian J. Goodfellow, Aaron Courville, Yoshua Bengio
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Abstract:Restricted Boltzmann machines (RBMs) are powerful machine learning models, but learning and some kinds of inference in the model require sampling-based approximations, which, in classical digital computers, are implemented using expensive MCMC. Physical computation offers the opportunity to reduce the cost of sampling by building physical systems whose natural dynamics correspond to drawing samples from the desired RBM distribution. Such a system avoids the burn-in and mixing cost of a Markov chain. However, hardware implementations of this variety usually entail limitations such as low-precision and limited range of the parameters and restrictions on the size and topology of the RBM. We conduct software simulations to determine how harmful each of these restrictions is. Our simulations are designed to reproduce aspects of the D-Wave quantum computer, but the issues we investigate arise in most forms of physical computation.
Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG)
Cite as: arXiv:1312.5258 [stat.ML]
  (or arXiv:1312.5258v2 [stat.ML] for this version)
  https://doi.org/10.48550/arXiv.1312.5258
Journal reference: Proc. AAAI 2014, pp. 1199-1205

Submission history

From: Vincent Dumoulin [view email]
[v1] Wed, 18 Dec 2013 18:30:51 UTC (720 KB)
[v2] Fri, 24 Oct 2014 19:16:14 UTC (630 KB)
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