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» On the Complexity of Train Assignment Problems
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131
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ICML
2009
IEEE
16 years 4 months ago
Large-scale deep unsupervised learning using graphics processors
The promise of unsupervised learning methods lies in their potential to use vast amounts of unlabeled data to learn complex, highly nonlinear models with millions of free paramete...
Rajat Raina, Anand Madhavan, Andrew Y. Ng
169
Voted
CVPR
2010
IEEE
16 years 2 days ago
SVM for Edge-Preserving Filtering
In this paper, we propose a new method to construct an edge-preserving filter which has very similar response to the bilateral filter. The bilateral filter is a normalized convolu...
Qingxiong Yang, Shengnan Wang, Narendra Ahuja
120
Voted
PAM
2010
Springer
15 years 10 months ago
A Learning-Based Approach for IP Geolocation
The ability to pinpoint the geographic location of IP hosts is compelling for applications such as on-line advertising and network attack diagnosis. While prior methods can accurat...
Brian Eriksson, Paul Barford, Joel Sommers, Robert...
128
Voted
ROBOCUP
2009
Springer
134views Robotics» more  ROBOCUP 2009»
15 years 10 months ago
Learning Complementary Multiagent Behaviors: A Case Study
As the reach of multiagent reinforcement learning extends to more and more complex tasks, it is likely that the diverse challenges posed by some of these tasks can only be address...
Shivaram Kalyanakrishnan, Peter Stone
142
Voted
PLDI
2003
ACM
15 years 9 months ago
Meta optimization: improving compiler heuristics with machine learning
Compiler writers have crafted many heuristics over the years to approximately solve NP-hard problems efficiently. Finding a heuristic that performs well on a broad range of applic...
Mark Stephenson, Saman P. Amarasinghe, Martin C. M...