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» Learning the Structure of Linear Latent Variable Models
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ICML
2010
IEEE
15 years 4 months ago
Gaussian Processes Multiple Instance Learning
This paper proposes a multiple instance learning (MIL) algorithm for Gaussian processes (GP). The GP-MIL model inherits two crucial benefits from GP: (i) a principle manner of lea...
Minyoung Kim, Fernando De la Torre
UAI
2000
15 years 4 months ago
Utilities as Random Variables: Density Estimation and Structure Discovery
Decision theory does not traditionally include uncertainty over utility functions. We argue that the a person's utility value for a given outcome can be treated as we treat o...
Urszula Chajewska, Daphne Koller
CVIU
2011
14 years 6 months ago
Single and sparse view 3D reconstruction by learning shape priors
In this paper, we aim to reconstruct free-form 3D models from only one or few silhouettes by learning the prior knowledge of a specific class of objects. Instead of heuristically...
Yu Chen, Roberto Cipolla
CVPR
2012
IEEE
13 years 5 months ago
Top-down visual saliency via joint CRF and dictionary learning
Top-down visual saliency facilities object localization by providing a discriminative representation of target objects and a probability map for reducing the search space. In this...
Jimei Yang, Ming-Hsuan Yang
ICML
2008
IEEE
16 years 4 months ago
Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity
Causal analysis of continuous-valued variables typically uses either autoregressive models or linear Gaussian Bayesian networks with instantaneous effects. Estimation of Gaussian ...
Aapo Hyvärinen, Patrik O. Hoyer, Shohei Shimi...