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» Learning Mixtures of DAG Models
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CVPR
2012
IEEE
12 years 11 days ago
Robust Boltzmann Machines for recognition and denoising
While Boltzmann Machines have been successful at unsupervised learning and density modeling of images and speech data, they can be very sensitive to noise in the data. In this pap...
Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hi...
ICML
2000
IEEE
14 years 10 months ago
Discovering Homogeneous Regions in Spatial Data through Competition
If all features causing heterogeneity were observed, a mixture of experts approach (Jacobs et al., 1991) is likely to be superior to using a single model. When unobserved or very n...
Slobodan Vucetic, Zoran Obradovic
ICML
2001
IEEE
14 years 10 months ago
Bayesian approaches to failure prediction for disk drives
Hard disk drive failures are rare but are often costly. The ability to predict failures is important to consumers, drive manufacturers, and computer system manufacturers alike. In...
Greg Hamerly, Charles Elkan
ICML
2010
IEEE
13 years 11 months ago
Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds
Inference in graphical models has emerged as a promising technique for planning. A recent approach to decision-theoretic planning in relational domains uses forward inference in d...
Tobias Lang, Marc Toussaint
TVCG
2012
191views Hardware» more  TVCG 2012»
12 years 11 days ago
Live Speech Driven Head-and-Eye Motion Generators
—This paper describes a fully automated framework to generate realistic head motion, eye gaze, and eyelid motion simultaneously based on live (or recorded) speech input. Its cent...
Binh Huy Le, Xiaohan Ma, Zhigang Deng