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» Algorithmic randomness of continuous functions
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170
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ICASSP
2011
IEEE
14 years 6 months ago
Automatic speech recognition using Hidden Conditional Neural Fields
Hidden Conditional Random Fields(HCRF) is a very promising approach to model speech. However, because HCRF computes the score of a hypothesis by summing up linearly weighted featu...
Yasuhisa Fujii, Kazumasa Yamamoto, Seiichi Nakagaw...
115
Voted
ICPR
2004
IEEE
16 years 3 months ago
Reconstructing a Dynamic Surface from Video Sequences Using Graph Cuts in 4D Space-Time
This paper is concerned with the problem of dynamically reconstructing the 3D surface of an object undergoing non-rigid motion. The problem is cast as reconstructing a continuous ...
Narendra Ahuja, Ning Xu, Tianli Yu
152
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JBI
2004
171views Bioinformatics» more  JBI 2004»
15 years 3 months ago
Consensus Clustering and Functional Interpretation of Gene Expression Data
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in assigning related gene-expression profiles to clusters. Obtaining a consensus s...
Paul Kellam, Stephen Swift, Allan Tucker, Veronica...
149
Voted
JMLR
2012
13 years 5 months ago
Random Search for Hyper-Parameter Optimization
Grid search and manual search are the most widely used strategies for hyper-parameter optimization. This paper shows empirically and theoretically that randomly chosen trials are ...
James Bergstra, Yoshua Bengio
111
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GECCO
2009
Springer
135views Optimization» more  GECCO 2009»
15 years 7 months ago
Benchmarking the (1+1)-ES with one-fifth success rule on the BBOB-2009 noisy testbed
The (1+1)-ES with one-fifth success rule is one of the first and simplest stochastic algorithm proposed for optimization on a continuous search space in a black-box scenario. In...
Anne Auger