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» On Deep Generative Models with Applications to Recognition
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JMLR
2010
192views more  JMLR 2010»
13 years 4 days 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
INTERSPEECH
2010
13 years 4 days ago
Binary coding of speech spectrograms using a deep auto-encoder
This paper reports our recent exploration of the layer-by-layer learning strategy for training a multi-layer generative model of patches of speech spectrograms. The top layer of t...
Li Deng, Michael L. Seltzer, Dong Yu, Alex Acero, ...
ICML
2009
IEEE
14 years 6 months ago
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks. Scaling such models to full-sized, high-dimensional images re...
Honglak Lee, Roger Grosse, Rajesh Ranganath, Andre...
JMLR
2012
11 years 7 months ago
Multiresolution Deep Belief Networks
Motivated by the observation that coarse and fine resolutions of an image reveal different structures in the underlying visual phenomenon, we present a model based on the Deep B...
Yichuan Tang, Abdel-rahman Mohamed
DSSCV
2005
Springer
13 years 11 months ago
Exploiting Deep Structure
Abstract. Blurring an image with a Gaussian of width σ and considering σ as an extra dimension, extends the image to an Gaussian scale space (GSS) image. In this GSS-image the is...
Arjan Kuijper