Restricted Boltzmann Machines (RBMs) have recently received much attention due to their potential to integrate more complex and deeper architectures. Despite their success, in many applications, training an RBM remains a tricky task. In this paper we present a learning adaptive step size method which accelerates its convergence. The results for the MNIST database demonstrate that the proposed method can drastically reduce the time necessary to achieve a good RBM reconstruction error. Moreover, the technique excels the fixed learning rate configurations, regardless of the momentum term used. © 2012 Springer-Verlag.
CITATION STYLE
Lopes, N., & Ribeiro, B. (2012). Improving convergence of restricted Boltzmann machines via a learning adaptive step size. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7441 LNCS, pp. 511–518). https://doi.org/10.1007/978-3-642-33275-3_63
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