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» Automatic Understanding of Signals
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ICASSP
2011
IEEE
14 years 7 months ago
Lattice-based unsupervised acoustic model training
Unsupervised acoustic model training has been successfully used to improve the performance of automatic speech recognition systems when only a small amount of manually transcribed...
Thiago Fraga-Silva, Jean-Luc Gauvain, Lori Lamel
ICASSP
2011
IEEE
14 years 7 months ago
Maximum marginal likelihood estimation for nonnegative dictionary learning
We describe an alternative to standard nonnegative matrix factorisation (NMF) for nonnegative dictionary learning. NMF with the Kullback-Leibler divergence can be seen as maximisa...
Onur Dikmen, Cédric Févotte
SIGCOMM
2010
ACM
15 years 4 months ago
NeuroPhone: brain-mobile phone interface using a wireless EEG headset
Neural signals are everywhere just like mobile phones. We propose to use neural signals to control mobile phones for hands-free, silent and effortless human-mobile interaction. Un...
Andrew T. Campbell, Tanzeem Choudhury, Shaohan Hu,...
RECOMB
2006
Springer
16 years 4 months ago
CONTRAlign: Discriminative Training for Protein Sequence Alignment
In this paper, we present CONTRAlign, an extensible and fully automatic framework for parameter learning and protein pairwise sequence alignment using pair conditional random field...
Chuong B. Do, Samuel S. Gross, Serafim Batzoglou
GECCO
2009
Springer
156views Optimization» more  GECCO 2009»
15 years 10 months ago
Characterizing the genetic programming environment for fifth (GPE5) on a high performance computing cluster
Solving complex, real-world problems with genetic programming (GP) can require extensive computing resources. However, the highly parallel nature of GP facilitates using a large n...
Kenneth Holladay