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ECML
2007
Springer
16 years 1 months ago
On Phase Transitions in Learning Sparse Networks
In this paper we study the identification of sparse interaction networks as a machine learning problem. Sparsity means that we are provided with a small data set and a high number...
Goele Hollanders, Geert Jan Bex, Marc Gyssens, Ron...
JMLR
2010
106views more  JMLR 2010»
15 years 1 months ago
Why Does Unsupervised Pre-training Help Deep Learning?
Much recent research has been devoted to learning algorithms for deep architectures such as Deep Belief Networks and stacks of auto-encoder variants, with impressive results obtai...
Dumitru Erhan, Yoshua Bengio, Aaron C. Courville, ...
ICIP
1999
IEEE
16 years 8 months ago
Integrating Stereo and Shape from Shading
This paper presents a new method for integrating di erent low level vision modules, stereo and shape from shading, in order to improve the 3D reconstruction of visible surfaces of...
Mostafa G.-H. Mostafa, Sameh M. Yamany, Aly A. Far...
TNN
1998
123views more  TNN 1998»
15 years 6 months ago
A general framework for adaptive processing of data structures
—A structured organization of information is typically required by symbolic processing. On the other hand, most connectionist models assume that data are organized according to r...
Paolo Frasconi, Marco Gori, Alessandro Sperduti
KDD
2003
ACM
217views Data Mining» more  KDD 2003»
16 years 7 months ago
Algorithms for estimating relative importance in networks
Large and complex graphs representing relationships among sets of entities are an increasingly common focus of interest in data analysis--examples include social networks, Web gra...
Scott White, Padhraic Smyth