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We present a mathematical construction for the restricted Boltzmann machine (RBM) that doesn't require specifying the number of hidden units.
The cascade-correlation learning architecture
Fahlman, S. E. and Lebiere, C. (1990) · 1989
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Training products of experts by minimizing contrastive divergence
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Restricted Boltzmann machines for collaborative filtering
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Implicit Mixtures of Restricted Boltzmann Machines
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On the quantitative analysis of Deep Belief Networks
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Training restricted Boltzmann machines using approximations to the likelihood gradient
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Geometry of the restricted Boltzmann machine
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Refinements of Universal Approximation Results for Deep Belief Networks and Restricted Boltzmann Machines
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Training Restricted Boltzmann Machines on Word Observations
Dahl, G. E., Adams, R. P., and Larochelle, H. (2012) · 2012
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Cardinality Restricted Boltzmann Machines
Swersky, K., Sutskever, I., Tarlow, D., Zemel, R. S., Salakhutdinov, R. R., and Adams, R. P. (2012) · 2012
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Online incremental feature learning with denoising autoencoders
Zhou, G., Sohn, K., and Lee, H. (2012) · 2012
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Enhanced gradient for training restricted Boltzmann machines
Cho, K., Raiko, T., and Ilin, A. (2013) · 2013
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Deep AutoRegressive Networks
Gregor, K., Mnih, A., and Wierstra, D. (2014) · 2014
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Iterative Neural Autoregressive Distribution
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Two distributed-state models for generating high-dimensional time series
Taylor, G. W., Hinton, G. E., and Roweis, S. T. (2011) · 2011
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Theano: new features and speed improvements
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Inductive Principles for Restricted Boltzmann Machine Learning
Marlin, B. M., Swersky, K., Chen, B., and de Freitas, N
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Learning Ordered Representations with Nested Dropout
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A Deep and Tractable Density Estimator
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