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» Learning the Structure of Linear Latent Variable Models
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IPMI
2009
Springer
15 years 8 months ago
Dense Registration with Deformation Priors
Abstract. In this paper we propose a novel approach to define task-driven regularization constraints in deformable image registration using learned deformation priors. Our method ...
Ben Glocker, Nikos Komodakis, Nassir Navab, Georgi...
TIP
2010
182views more  TIP 2010»
14 years 10 months ago
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the ...
Feiping Nie, Dong Xu, Ivor Wai-Hung Tsang, Changsh...
106
Voted
PLDI
2010
ACM
16 years 24 days ago
Complete Functional Synthesis
Synthesis of program fragments from specifications can make programs easier to write and easier to reason about. To integrate synthesis into programming languages, synthesis algor...
Viktor Kuncak, Mika l Mayer, Ruzica Piskac, Philip...
142
Voted
SIGECOM
2006
ACM
139views ECommerce» more  SIGECOM 2006»
15 years 9 months ago
Playing games in many possible worlds
In traditional game theory, players are typically endowed with exogenously given knowledge of the structure of the game—either full omniscient knowledge or partial but fixed in...
Matt Lepinski, David Liben-Nowell, Seth Gilbert, A...
140
Voted
ESANN
2004
15 years 4 months ago
Sparse LS-SVMs using additive regularization with a penalized validation criterion
This paper is based on a new way for determining the regularization trade-off in least squares support vector machines (LS-SVMs) via a mechanism of additive regularization which ha...
Kristiaan Pelckmans, Johan A. K. Suykens, Bart De ...