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CORR
2010
Springer
153views Education» more  CORR 2010»
15 years 7 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...
FGCS
2006
135views more  FGCS 2006»
15 years 7 months ago
Scaling applications to massively parallel machines using Projections performance analysis tool
Some of the most challenging applications to parallelize scalably are the ones that present a relatively small amount of computation per iteration. Multiple interacting performanc...
Laxmikant V. Kalé, Gengbin Zheng, Chee Wai ...
CCR
2007
104views more  CCR 2007»
15 years 7 months ago
Internet clean-slate design: what and why?
Many believe that it is impossible to resolve the challenges facing today’s Internet without rethinking the fundamental assumptions and design decisions underlying its current a...
Anja Feldmann
CN
2007
94views more  CN 2007»
15 years 7 months ago
Modeling and generating realistic streaming media server workloads
Currently, Internet hosting centers and content distribution networks leverage statistical multiplexing to meet the performance requirements of a number of competing hosted networ...
Wenting Tang, Yun Fu, Ludmila Cherkasova, Amin Vah...
ML
2008
ACM
15 years 7 months ago
Margin-based first-order rule learning
Abstract We present a new margin-based approach to first-order rule learning. The approach addresses many of the prominent challenges in first-order rule learning, such as the comp...
Ulrich Rückert, Stefan Kramer