On the Challenges of Physical Implementations of RBMs

18 Dec 2013Vincent DumoulinIan J. GoodfellowAaron CourvilleYoshua Bengio

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... (read more)

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