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» Using Problems to Learn Service-Oriented Computing
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CVPR
2011
IEEE
15 years 1 months ago
Iterative Quantization: A Procrustean Approach to Learning Binary Codes
This paper addresses the problem of learning similaritypreserving binary codes for efficient retrieval in large-scale image collections. We propose a simple and efficient altern...
Yunchao Gong, Svetlana Lazebnik
CVPR
2011
IEEE
14 years 8 months ago
Earth Mover’s Prototypes: a Convex Learning Approach for Discovering Activity Patterns in Dynamic Scenes
We present a novel approach for automatically discovering spatio-temporal patterns in complex dynamic scenes. Similarly to recent non-object centric methods, we use low level visu...
Elisa Ricci, Gloria Zen
ECCV
2010
Springer
15 years 6 months ago
Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning
Abstract. We study the problem of multimodal dimensionality reduction assuming that data samples can be missing at training time, and not all data modalities may be present at appl...
Christoph H. Lampert, Oliver Krömer
ACL
2010
15 years 2 months ago
Global Learning of Focused Entailment Graphs
We propose a global algorithm for learning entailment relations between predicates. We define a graph structure over predicates that represents entailment relations as directed ed...
Jonathan Berant, Ido Dagan, Jacob Goldberger
JMLR
2010
147views more  JMLR 2010»
14 years 11 months ago
Spectral Regularization Algorithms for Learning Large Incomplete Matrices
We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we...
Rahul Mazumder, Trevor Hastie, Robert Tibshirani