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» Learning from Highly Structured Data by Decomposition
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CIKM
2010
Springer
14 years 8 months ago
Yes we can: simplex volume maximization for descriptive web-scale matrix factorization
Matrix factorization methods are among the most common techniques for detecting latent components in data. Popular examples include the Singular Value Decomposition or Nonnegative...
Christian Thurau, Kristian Kersting, Christian Bau...
CGF
2007
128views more  CGF 2007»
14 years 10 months ago
Stackless KD-Tree Traversal for High Performance GPU Ray Tracing
Significant advances have been achieved for realtime ray tracing recently, but realtime performance for complex scenes still requires large computational resources not yet availa...
Stefan Popov, Johannes Günther, Hans-Peter Se...
MMM
2011
Springer
251views Multimedia» more  MMM 2011»
14 years 1 months ago
Randomly Projected KD-Trees with Distance Metric Learning for Image Retrieval
Abstract. Efficient nearest neighbor (NN) search techniques for highdimensional data are crucial to content-based image retrieval (CBIR). Traditional data structures (e.g., kd-tree...
Pengcheng Wu, Steven C. H. Hoi, Duc Dung Nguyen, Y...
94
Voted
EMNLP
2009
14 years 8 months ago
Convolution Kernels on Constituent, Dependency and Sequential Structures for Relation Extraction
This paper explores the use of innovative kernels based on syntactic and semantic structures for a target relation extraction task. Syntax is derived from constituent and dependen...
Truc-Vien T. Nguyen, Alessandro Moschitti, Giusepp...
70
Voted
DNA
2005
Springer
118views Bioinformatics» more  DNA 2005»
15 years 3 months ago
Molecular Learning of wDNF Formulae
We introduce a class of generalized DNF formulae called wDNF or weighted disjunctive normal form, and present a molecular algorithm that learns a wDNF formula from training example...
Byoung-Tak Zhang, Ha-Young Jang