Soft-Deep Boltzmann Machines

Preprint English OPEN
Kiwaki, Taichi;
(2015)
  • Subject: Statistics - Machine Learning | Computer Science - Neural and Evolutionary Computing | Computer Science - Learning

We present a layered Boltzmann machine (BM) that can better exploit the advantages of a distributed representation. It is widely believed that deep BMs (DBMs) have far greater representational power than its shallow counterpart, restricted Boltzmann machines (RBMs). How... View more
  • References (36)
    36 references, page 1 of 4

    [1] Geoffrey E Hinton. Distributed Representations. In James L McClelland and David E Rumelhart, editors, Parallel Distributed Processing. MIT Press, 1986.

    [2] Yoshua Bengio. Learning Deep Architectures for AI. Now Publishers Inc, October 2009.

    [3] Razvan Pascanu, Guido Montu´far, and Yoshua Bengio. On the number of response regions of deep feed forward networks with piece-wise linear activations. arXiv.org, December 2013.

    [4] Guido Montu´far, Razvan Pascanu, KyungHyun Cho, and Yoshua Bengio. On the Number of Linear Regions of Deep Neural Networks. In Advances in Neural Information Processing Systems 27, 2014.

    [5] Paul Smolensky. Information Processing in Dynamical Systems: Foundations of Harmony Theory. In David E Rumelhart and James L McClelland, editors, Parallel Distributed Processing:Explorations in the Microstructure of Cognition: Foundations, pages 194-281. MIT press, 1986.

    [6] Ruslan Salakhutdinov and Geoffrey Hinton. Deep Boltzmann machines. In Proceedings of the 12th International Conference on Artificial Intelligence and Statistics, pages 448-455, 2009.

    [7] Geoffrey E Hinton and Terrence J Sejnowski. Learning and Relearning in Boltzmann Machines. In David E Rumelhart and James L McClelland, editors, Parallel Distributed Processing:Explorations in the Microstructure of Cognition: Foundations, pages 282-317. MIT press, 1986.

    [8] James Martens, Arkadev Chattopadhyay, Toniann Pitassi, and Richard Zemel. On the Representational Efficiency of Restricted Boltzmann Machines. In Advances in Neural Information Processing Systems 26, pages 1-21, 2013.

    [9] Christopher M Bishop. Pattern Recognition and Machine Learning. Springer, 2006.

    [10] Yann LeCun, Le´on Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. In Proceedings of the IEEE, pages 2278-2324, 1998.

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