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» A Function Representation for Learning in Banach Spaces
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NIPS
2008
13 years 9 months ago
Stochastic Relational Models for Large-scale Dyadic Data using MCMC
Stochastic relational models (SRMs) [15] provide a rich family of choices for learning and predicting dyadic data between two sets of entities. The models generalize matrix factor...
Shenghuo Zhu, Kai Yu, Yihong Gong
TMI
2010
208views more  TMI 2010»
13 years 2 months ago
Patient-Specific Modeling and Quantification of the Aortic and Mitral Valves From 4-D Cardiac CT and TEE
As decisions in cardiology increasingly rely on non-invasive methods, fast and precise image processing tools have become a crucial component of the analysis workflow. To the best ...
Razvan Ioan Ionasec, Ingmar Voigt, Bogdan Georgesc...
CGF
2007
135views more  CGF 2007»
13 years 7 months ago
Prediction of Individual Non-Linear Aging Trajectories of Faces
Represented in a Morphable Model, 3D faces follow curved trajectories in face space as they age. We present a novel algorithm that computes the individual aging trajectories for g...
Kristina Scherbaum, Martin Sunkel, Hans-Peter Seid...
COMPGEOM
2011
ACM
12 years 11 months ago
Comparing distributions and shapes using the kernel distance
Starting with a similarity function between objects, it is possible to define a distance metric (the kernel distance) on pairs of objects, and more generally on probability distr...
Sarang C. Joshi, Raj Varma Kommaraju, Jeff M. Phil...
ICML
2000
IEEE
14 years 8 months ago
Rates of Convergence for Variable Resolution Schemes in Optimal Control
This paper presents a general method to derive tight rates of convergence for numerical approximations in optimal control when we consider variable resolution grids. We study the ...
Andrew W. Moore, Rémi Munos