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» Learning Regular Expressions from Noisy Sequences
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MLMI
2004
Springer
15 years 4 months ago
Mapping from Speech to Images Using Continuous State Space Models
In this paper a system that transforms speech waveforms to animated faces are proposed. The system relies on continuous state space models to perform the mapping, this makes it po...
Tue Lehn-Schiøler, Lars Kai Hansen, Jan Lar...
GECCO
2009
Springer
173views Optimization» more  GECCO 2009»
15 years 5 months ago
Creating regular expressions as mRNA motifs with GP to predict human exon splitting
Low correlation between mRNA concentrations measured at different locations for the same exon show many current Ensembl exon definitions are incomplete. Automatically created pa...
William B. Langdon, Joanna Rowsell, Andrew P. Harr...
91
Voted
RECOMB
2007
Springer
15 years 11 months ago
Learning Gene Regulatory Networks via Globally Regularized Risk Minimization
Learning the structure of a gene regulatory network from time-series gene expression data is a significant challenge. Most approaches proposed in the literature to date attempt to ...
Yuhong Guo, Dale Schuurmans
95
Voted
ECCV
2008
Springer
16 years 29 days ago
A Generative Shape Regularization Model for Robust Face Alignment
In this paper, we present a robust face alignment system that is capable of dealing with exaggerating expressions, large occlusions, and a wide variety of image noises. The robustn...
Leon Gu, Takeo Kanade
80
Voted
ICML
2006
IEEE
15 years 12 months ago
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data. In speech recognition, for example, an acoustic signal is...
Alex Graves, Faustino J. Gomez, Jürgen Schmid...