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» Learning Gaussian processes from multiple tasks
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145
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EMNLP
2011
14 years 3 months ago
Lateen EM: Unsupervised Training with Multiple Objectives, Applied to Dependency Grammar Induction
We present new training methods that aim to mitigate local optima and slow convergence in unsupervised training by using additional imperfect objectives. In its simplest form, lat...
Valentin I. Spitkovsky, Hiyan Alshawi, Daniel Jura...
124
Voted
ICASSP
2008
IEEE
15 years 10 months ago
Analysis-by-synthesis features for speech recognition
We present a framework for speech recognition that accounts for hidden articulatory information. We model the articulatory space using a codebook of articulatory configurations g...
Ziad Al Bawab, Bhiksha Raj, Richard M. Stern
156
Voted
IJCNN
2008
IEEE
15 years 10 months ago
A neural network approach to ordinal regression
— Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe an effective approach to adapt a traditional ...
Jianlin Cheng, Zheng Wang, Gianluca Pollastri
146
Voted
ISBI
2006
IEEE
15 years 9 months ago
Non-contact fluorescence optical tomography with scanning area illumination
This contribution describes a novel non-contact fluorescence optical tomography scheme which utilizes multiple area illumination patterns, to reduce the illposedness of the inver...
Amit Joshi, Wolfgang Bangerth, Eva M. Sevick-Murac...
136
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NECO
2010
136views more  NECO 2010»
15 years 2 months ago
Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines
To allow the hidden units of a restricted Boltzmann machine to model the transformation between two successive images, Memisevic and Hinton (2007) introduced three-way multiplicat...
Roland Memisevic, Geoffrey E. Hinton