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ECCV
2008
Springer
16 years 6 months ago
Learning Optical Flow
Assumptions of brightness constancy and spatial smoothness underlie most optical flow estimation methods. In contrast to standard heuristic formulations, we learn a statistical mod...
Deqing Sun, Stefan Roth, J. P. Lewis, Michael J. B...
ILP
2005
Springer
15 years 10 months ago
Machine Learning for Systems Biology
In this paper we survey work being conducted at Imperial College on the use of machine learning to build Systems Biology models of the effects of toxins on biochemical pathways. Se...
Stephen Muggleton
160
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ACL
2008
15 years 6 months ago
Soft Syntactic Constraints for Hierarchical Phrased-Based Translation
In adding syntax to statistical MT, there is a tradeoff between taking advantage of linguistic analysis, versus allowing the model to exploit linguistically unmotivated mappings l...
Yuval Marton, Philip Resnik
CVPR
2004
IEEE
16 years 7 months ago
Bayesian Assembly of 3D Axially Symmetric Shapes from Fragments
We present a complete system for the purpose of automatically assembling 3D pots given 3D measurements of their fragments commonly called sherds. A Bayesian approach is formulated...
Andrew R. Willis, David B. Cooper
CORR
2012
Springer
183views Education» more  CORR 2012»
14 years 19 days ago
Learning Determinantal Point Processes
Determinantal point processes (DPPs), which arise in random matrix theory and quantum physics, are natural models for subset selection problems where diversity is preferred. Among...
Alex Kulesza, Ben Taskar