Abstract
This paper proposes a continuous stochastic generative model that offers an improved ability to model analogue data, with a simple and reliable learning algorithm. The architecture forms a continuous restricted Boltzmann Machine, with a novel learning algorithm. The capabilities of the model are demonstrated with both artificial and real data. © Springer-Verlag Berlin Heidelberg 2002.
Cite
CITATION STYLE
Chen, H., & Murray, A. (2002). A continuous restricted Boltzmann Machine with a hardware-amenable learning algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 358–363). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_58
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