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A Method for the Efficient Design of Boltzmann Machines for Classiffication Problems

Neural Information Processing SystemsApr-6-2023, 19:42:14 GMT

We introduce a method for the efficient design of a Boltzmann machine (or a Hopfield net) that computes an arbitrary given Boolean function f . This method is based on an efficient simulation of acyclic circuits with threshold gates by Boltzmann machines. As a consequence we can show that various concrete Boolean functions f that are relevant for classification problems can be computed by scalable Boltzmann machines that are guaranteed to converge to their global maximum configuration with high probability after constantly many steps.

boltzmann machine, classiffication problem, efficient design

Neural Information Processing Systems

Technology: Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Undirected Networks > Markov Models (1.00)

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