922 search results - page 90 / 185 » Learning Gaussian Process Models from Uncertain Data |
147
Voted
NIPS
2000
15 years 3 months ago
2000
Learning of a smooth but nonparametric probability density can be regularized using methods of Quantum Field Theory. We implement a field theoretic prior numerically, test its eff...
127
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EMNLP
2010
15 years 16 days ago
2010
This paper addresses the problem of learning to map sentences to logical form, given training data consisting of natural language sentences paired with logical representations of ...
139
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ICASSP
2011
IEEE
14 years 6 months ago
2011
IEEE
Recently, Multiple Background Models (M-BMs) [1, 2] have been shown to be useful in speaker verification, where the M-BMs are formed based on different Vocal Tract Lengths (VTLs)...
133
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JAIR
2002
15 years 2 months ago
2002
Hidden Markov models hmms and partially observable Markov decision processes pomdps provide useful tools for modeling dynamical systems. They are particularly useful for represent...
124
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NIPS
2000
15 years 3 months ago
2000
Bayesian networks are graphical representations of probability distributions. In virtually all of the work on learning these networks, the assumption is that we are presented with...