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JMLR
2012
10 years 8 days 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 speci´Čücally training on this task, evaluating m...
Jyri J. Kivinen, Christopher K. I. Williams
CVPR
2012
IEEE
10 years 7 days ago
Robust Boltzmann Machines for recognition and denoising
While Boltzmann Machines have been successful at unsupervised learning and density modeling of images and speech data, they can be very sensitive to noise in the data. In this pap...
Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hi...
ICML
2007
IEEE
12 years 10 months ago
Restricted Boltzmann machines for collaborative filtering
Most of the existing approaches to collaborative filtering cannot handle very large data sets. In this paper we show how a class of two-layer undirected graphical models, called R...
Ruslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hin...
ICML
2009
IEEE
12 years 10 months ago
Factored conditional restricted Boltzmann Machines for modeling motion style
The Conditional Restricted Boltzmann Machine (CRBM) is a recently proposed model for time series that has a rich, distributed hidden state and permits simple, exact inference. We ...
Graham W. Taylor, Geoffrey E. Hinton
NECO
2010
136views more  NECO 2010»
11 years 8 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
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