226 search results - page 20 / 46 » How to learn a graph from smooth signals |
171
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MICAI
2010
Springer
15 years 4 months ago
2010
Springer
The full deployment of service robots in daily activities will require the robot to adapt to the needs of non-expert users, particularly, to learn how to perform new tasks from “...
158
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ICML
2006
IEEE
16 years 6 months ago
2006
IEEE
The Gaussian process latent variable model (GP-LVM) is a generative approach to nonlinear low dimensional embedding, that provides a smooth probabilistic mapping from latent to da...
142
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COLING
2008
15 years 7 months ago
2008
In this paper we describe an empirical study of human-human multi-tasking dialogues (MTD), where people perform multiple verbal tasks overlapped in time. We examined how conversan...
187
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ICASSP
2010
IEEE
15 years 6 months ago
2010
IEEE
Amplitude demodulation is an ill-posed problem and so it is natural to treat it from a Bayesian viewpoint, inferring the most likely carrier and envelope under probabilistic const...
174
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WSDM
2009
ACM
16 years 26 days ago
2009
ACM
The ranking function used by search engines to order results is learned from labeled training data. Each training point is a (query, URL) pair that is labeled by a human judge who...