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» GraphLab: A New Framework for Parallel Machine Learning
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HPDC
2010
IEEE
13 years 9 months ago
Cloud computing paradigms for pleasingly parallel biomedical applications
Cloud computing offers new approaches for scientific computing that leverage the major commercial hardware and software investment in this area. Closely coupled applications are s...
Thilina Gunarathne, Tak-Lon Wu, Judy Qiu, Geoffrey...
POPL
1997
ACM
14 years 29 days ago
Synchronization Transformations for Parallel Computing
As parallel machines become part of the mainstream computing environment, compilers will need to apply synchronization optimizations to deliver e cient parallel software. This pap...
Pedro C. Diniz, Martin C. Rinard
ICML
2001
IEEE
14 years 9 months ago
Off-Policy Temporal Difference Learning with Function Approximation
We introduce the first algorithm for off-policy temporal-difference learning that is stable with linear function approximation. Off-policy learning is of interest because it forms...
Doina Precup, Richard S. Sutton, Sanjoy Dasgupta
NIPS
2008
13 years 10 months ago
Kernel Measures of Independence for non-iid Data
Many machine learning algorithms can be formulated in the framework of statistical independence such as the Hilbert Schmidt Independence Criterion. In this paper, we extend this c...
Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smo...
ICPR
2008
IEEE
14 years 10 months ago
SVMs, Gaussian mixtures, and their generative/discriminative fusion
We present a new technique that employs support vector machines and Gaussian mixture densities to create a generative/discriminative joint classifier. In the past, several approac...
Georg Heigold, Hermann Ney, Thomas Deselaers