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» The complexity of learning SUBSEQ(A)
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CVPR
2008
IEEE
16 years 8 months ago
Local deformation models for monocular 3D shape recovery
Without a deformation model, monocular 3D shape recovery of deformable surfaces is severly under-constrained. Even when the image information is rich enough, prior knowledge of th...
Mathieu Salzmann, Raquel Urtasun, Pascal Fua
ICIP
2006
IEEE
16 years 8 months ago
Local Discriminant Embedding with Tensor Representation
We present a subspace learning method, called Local Discriminant Embedding with Tensor representation (LDET), that addresses simultaneously the generalization and data representat...
Jian Xia, Dit-Yan Yeung, Guang Dai
ICML
2008
IEEE
16 years 7 months ago
On the quantitative analysis of deep belief networks
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
Ruslan Salakhutdinov, Iain Murray
KDD
2009
ACM
207views Data Mining» more  KDD 2009»
16 years 7 months ago
DynaMMo: mining and summarization of coevolving sequences with missing values
Given multiple time sequences with missing values, we propose DynaMMo which summarizes, compresses, and finds latent variables. The idea is to discover hidden variables and learn ...
Lei Li, James McCann, Nancy S. Pollard, Christos F...
ALT
2005
Springer
16 years 3 months ago
Teaching Learners with Restricted Mind Changes
Within learning theory teaching has been studied in various ways. In a common variant the teacher has to teach all learners that are restricted to output only consistent hypotheses...
Frank J. Balbach, Thomas Zeugmann