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» Learning Compressible Models
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ICCV
2009
IEEE
14 years 7 months ago
Learning with dynamic group sparsity
This paper investigates a new learning formulation called dynamic group sparsity. It is a natural extension of the standard sparsity concept in compressive sensing, and is motivat...
Junzhou Huang, Xiaolei Huang, Dimitris N. Metaxas
ACMSE
2010
ACM
14 years 5 months ago
Learning to rank using 1-norm regularization and convex hull reduction
The ranking problem appears in many areas of study such as customer rating, social science, economics, and information retrieval. Ranking can be formulated as a classification pro...
Xiaofei Nan, Yixin Chen, Xin Dang, Dawn Wilkins
CORR
2010
Springer
153views Education» more  CORR 2010»
14 years 10 months ago
GraphLab: A New Framework for Parallel Machine Learning
Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insuf...
Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny B...
ML
2006
ACM
187views Machine Learning» more  ML 2006»
14 years 10 months ago
Modeling, analyzing, and synthesizing expressive piano performance with graphical models
Abstract Trained musicians intuitively produce expressive variations that add to their audience's enjoyment. However, there is little quantitative information about the kinds ...
Graham Grindlay, David P. Helmbold
ALT
2001
Springer
15 years 7 months ago
Real-Valued Multiple-Instance Learning with Queries
While there has been a significant amount of theoretical and empirical research on the multiple-instance learning model, most of this research is for concept learning. However, f...
Daniel R. Dooly, Sally A. Goldman, Stephen Kwek