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» Learning with Deep Cascades
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106
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ICML
2010
IEEE
15 years 1 months ago
Deep networks for robust visual recognition
Deep Belief Networks (DBNs) are hierarchical generative models which have been used successfully to model high dimensional visual data. However, they are not robust to common vari...
Yichuan Tang, Chris Eliasmith
115
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CORR
2010
Springer
147views Education» more  CORR 2010»
15 years 23 days ago
Modeling the structure and evolution of discussion cascades
We analyze the structure and evolution of discussion cascades in four popular websites: Slashdot, Barrapunto, Meneame and Wikipedia. Despite the big heterogeneities between these ...
Vicenç Gómez, Hilbert J. Kappen, And...
CVPR
2010
IEEE
15 years 9 months ago
Cascade Object Detection with Deformable Part Models
We describe a general method for building cascade classifiers from part-based deformable models such as pictorial structures. We focus primarily on the case of star-structured mod...
Pedro Felzenszwalb, Ross Girshick, David McAlleste...
NECO
2008
146views more  NECO 2008»
15 years 19 days ago
Deep, Narrow Sigmoid Belief Networks Are Universal Approximators
In this paper we show that exponentially deep belief networks [3, 7, 4] can approximate any distribution over binary vectors to arbitrary accuracy, even when the width of each lay...
Ilya Sutskever, Geoffrey E. Hinton
110
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NIPS
2008
15 years 2 months ago
Cascaded Classification Models: Combining Models for Holistic Scene Understanding
One of the original goals of computer vision was to fully understand a natural scene. This requires solving several sub-problems simultaneously, including object detection, region...
Geremy Heitz, Stephen Gould, Ashutosh Saxena, Daph...