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JMLR
2010
202views more  JMLR 2010»
13 years 3 months ago
Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
ISCAS
2005
IEEE
154views Hardware» more  ISCAS 2005»
14 years 2 months ago
Back propagation learning of neural networks with chaotically-selected affordable neurons
— Cell assembly is one of explanations of information processing in the brain, in which an information is represented by a firing space pattern of a group of plural neurons. On ...
Yoko Uwate, Yoshifumi Nishio
IJCAI
1997
13 years 10 months ago
Learning Topological Maps with Weak Local Odometric Information
cal maps provide a useful abstraction for robotic navigation and planning. Although stochastic mapscan theoreticallybe learned using the Baum-Welch algorithm,without strong prior ...
Hagit Shatkay, Leslie Pack Kaelbling
NIPS
1997
13 years 10 months ago
Learning Human-like Knowledge by Singular Value Decomposition: A Progress Report
Singular value decomposition (SVD) can be viewed as a method for unsupervised training of a network that associates two classes of events reciprocally by linear connections throug...
Thomas K. Landauer, Darrell Laham, Peter W. Foltz
PVLDB
2008
117views more  PVLDB 2008»
13 years 8 months ago
Learning to extract form labels
In this paper we describe a new approach to extract element labels from Web form interfaces. Having these labels is a requirement for several techniques that attempt to retrieve a...
Hoa Nguyen, Thanh Hoang Nguyen, Juliana Freire