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ECCV
2008
Springer
14 years 9 months ago
Learning for Optical Flow Using Stochastic Optimization
Abstract. We present a technique for learning the parameters of a continuousstate Markov random field (MRF) model of optical flow, by minimizing the training loss for a set of grou...
Yunpeng Li, Daniel P. Huttenlocher
CCE
2010
13 years 5 months ago
A simple heuristic for reducing the number of scenarios in two-stage stochastic programming
In this work we address the problem of solving multiscenario optimization models that are deterministic equivalents of two-stage stochastic programs. We present a heuristic approx...
Ramkumar Karuppiah, Mariano Martín, Ignacio...
TACAS
2010
Springer
251views Algorithms» more  TACAS 2010»
14 years 1 months ago
Approximating the Pareto Front of Multi-criteria Optimization Problems
We propose a general methodology for approximating the Pareto front of multi-criteria optimization problems. Our search-based methodology consists of submitting queries to a constr...
Julien Legriel, Colas Le Guernic, Scott Cotton, Od...
TWC
2008
135views more  TWC 2008»
13 years 7 months ago
Optimal Distributed Stochastic Routing Algorithms for Wireless Multihop Networks
A novel framework was introduced recently for stochastic routing in wireless multihop networks, whereby each node selects a neighbor to forward a packet according to a probability...
Alejandro Ribeiro, Nikolas D. Sidiropoulos, Georgi...
ICRA
2008
IEEE
197views Robotics» more  ICRA 2008»
14 years 2 months ago
A Bayesian framework for optimal motion planning with uncertainty
— Modeling robot motion planning with uncertainty in a Bayesian framework leads to a computationally intractable stochastic control problem. We seek hypotheses that can justify a...
Andrea Censi, Daniele Calisi, Alessandro De Luca, ...