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» Kauffman networks: analysis and applications
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UAI
2008
15 years 7 months ago
Projected Subgradient Methods for Learning Sparse Gaussians
Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our a...
John Duchi, Stephen Gould, Daphne Koller
RIAO
2000
15 years 7 months ago
Learning for Sequence Extraction Tasks
We consider the application of machine learning techniques for sequence modeling to Information Retrieval (IR) and surface Information Extraction (IE) tasks. We introduce a generi...
Massih-Reza Amini, Hugo Zaragoza, Patrick Gallinar...
WSC
1997
15 years 7 months ago
A Simulation Environment for the Coordinated Operation of Multiple Autonomous Underwater Vehicles
A simulation environment of the coordinated operation of multiple Autonomous Underwater Vehicles (AUVs) is presented. The primary application of this simulation environment is the...
João Borges de Sousa, Aleks Göllü
NIPS
1996
15 years 7 months ago
Why did TD-Gammon Work?
Although TD-Gammon is one of the major successes in machine learning, it has not led to similar impressive breakthroughs in temporal difference learning for other applications or ...
Jordan B. Pollack, Alan D. Blair
GECCO
2008
Springer
156views Optimization» more  GECCO 2008»
15 years 7 months ago
Computing minimum cuts by randomized search heuristics
We study the minimum s-t-cut problem in graphs with costs on the edges in the context of evolutionary algorithms. Minimum cut problems belong to the class of basic network optimiz...
Frank Neumann, Joachim Reichel, Martin Skutella