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» Learning the Structure of Linear Latent Variable Models
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ICCV
2009
IEEE
13 years 6 months ago
Bayesian Poisson regression for crowd counting
Poisson regression models the noisy output of a counting function as a Poisson random variable, with a log-mean parameter that is a linear function of the input vector. In this wo...
Antoni B. Chan, Nuno Vasconcelos
CORR
2011
Springer
169views Education» more  CORR 2011»
13 years 3 months ago
Streaming Tree Transducers
We introduce streaming tree transducers as an analyzable and expressive model for transforming hierarchically structured data in a single pass. Given a linear encoding of the inpu...
Rajeev Alur, Loris D'Antoni
ICANN
2009
Springer
13 years 6 months ago
MINLIP: Efficient Learning of Transformation Models
Abstract. This paper studies a risk minimization approach to estimate a transformation model from noisy observations. It is argued that transformation models are a natural candidat...
Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. ...
KDD
2009
ACM
203views Data Mining» more  KDD 2009»
14 years 9 months ago
Characterizing individual communication patterns
The increasing availability of electronic communication data, such as that arising from e-mail exchange, presents social and information scientists with new possibilities for char...
R. Dean Malmgren, Jake M. Hofman, Luis A. N. Amara...
NIPS
2003
13 years 10 months ago
Factorization with Uncertainty and Missing Data: Exploiting Temporal Coherence
The problem of “Structure From Motion” is a central problem in vision: given the 2D locations of certain points we wish to recover the camera motion and the 3D coordinates of ...
Amit Gruber, Yair Weiss