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.
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Chen, H., Murray, A. (2002). A Continuous Restricted Boltzmann Machine with a Hardware- Amenable Learning Algorithm. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_58
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DOI: https://doi.org/10.1007/3-540-46084-5_58
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