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JMLR
2010
192views more  JMLR 2010»
14 years 6 months ago
Efficient Learning of Deep Boltzmann Machines
We present a new approximate inference algorithm for Deep Boltzmann Machines (DBM's), a generative model with many layers of hidden variables. The algorithm learns a separate...
Ruslan Salakhutdinov, Hugo Larochelle
JMLR
2012
13 years 1 months ago
Multiple Texture Boltzmann Machines
We assess the generative power of the mPoTmodel of [10] with tiled-convolutional weight sharing as a model for visual textures by specifically training on this task, evaluating m...
Jyri J. Kivinen, Christopher K. I. Williams
NECO
2010
136views more  NECO 2010»
14 years 9 months ago
Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines
To allow the hidden units of a restricted Boltzmann machine to model the transformation between two successive images, Memisevic and Hinton (2007) introduced three-way multiplicat...
Roland Memisevic, Geoffrey E. Hinton
ICANN
2010
Springer
14 years 11 months ago
Deep Bottleneck Classifiers in Supervised Dimension Reduction
Deep autoencoder networks have successfully been applied in unsupervised dimension reduction. The autoencoder has a "bottleneck" middle layer of only a few hidden units, ...
Elina Parviainen
DATAMINE
2010
122views more  DATAMINE 2010»
14 years 11 months ago
Three naive Bayes approaches for discrimination-free classification
In this paper, we investigate how to modify the Naive Bayes classifier in order to perform classification that is restricted to be independent with respect to a given sensitive att...
Toon Calders, Sicco Verwer