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JACM
2006
99views more  JACM 2006»
14 years 11 months ago
Finding a maximum likelihood tree is hard
Abstract. Maximum likelihood (ML) is an increasingly popular optimality criterion for selecting evolutionary trees [Felsenstein 1981]. Finding optimal ML trees appears to be a very...
Benny Chor, Tamir Tuller
BMCBI
2006
175views more  BMCBI 2006»
14 years 11 months ago
Parameter estimation for stiff equations of biosystems using radial basis function networks
Background: The modeling of dynamic systems requires estimating kinetic parameters from experimentally measured time-courses. Conventional global optimization methods used for par...
Yoshiya Matsubara, Shinichi Kikuchi, Masahiro Sugi...
ICML
2009
IEEE
16 years 17 days 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
CGI
2009
IEEE
15 years 3 months ago
Easy integral surfaces: a fast, quad-based stream and path surface algorithm
Despite the clear benefits that stream and path surfaces bring when visualizing 3D vector fields, their use in both industry and for research has not proliferated. This is due, in...
Tony McLoughlin, Robert S. Laramee, Eugene Zhang
CORR
2008
Springer
88views Education» more  CORR 2008»
14 years 12 months ago
Lower bounds for distributed markov chain problems
We study the worst-case communication complexity of distributed algorithms computing a path problem based on stationary distributions of random walks in a network G with the caveat...
Rahul Sami, Andy Twigg