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ICML
2009
IEEE
16 years 20 days 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...
ACL
2011
14 years 3 months ago
From Bilingual Dictionaries to Interlingual Document Representations
Mapping documents into an interlingual representation can help bridge the language barrier of a cross-lingual corpus. Previous approaches use aligned documents as training data to...
Jagadeesh Jagarlamudi, Hal Daumé III, Ragha...
ICPR
2004
IEEE
16 years 29 days ago
Learning High-level Independent Components of Images through a Spectral Representation
Statistical methods, such as independent component analysis, have been successful in learning local low-level features from natural image data. Here we extend these methods for le...
Aapo Hyvärinen, Jussi T. Lindgren
ICPR
2010
IEEE
15 years 3 months ago
Learning a Joint Manifold Representation from Multiple Data Sets
—The problem we address in the paper is how to learn a joint representation from data lying on multiple manifolds. We are given multiple data sets and there is an underlying comm...
Marwan Torki, Ahmed Elgammal, Chan-Su Lee
AUSAI
2008
Springer
15 years 1 months ago
Learning Object Representations Using Sequential Patterns
This paper explores the use of alternating sequential patterns of local features and saccading actions to learn robust and compact object representations. The temporal encoding rep...
Nobuyuki Morioka