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» Learning for Optical Flow Using Stochastic Optimization
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PE
2010
Springer
212views Optimization» more  PE 2010»
14 years 4 months ago
Modeling TCP throughput: An elaborated large-deviations-based model and its empirical validation
In today's Internet, a large part of the traffic is carried using the TCP transport protocol. Characterization of the variations of TCP traffic is thus a major challenge, bot...
Patrick Loiseau, Paulo Gonçalves, Julien Ba...
91
Voted
UAI
2008
14 years 11 months ago
CORL: A Continuous-state Offset-dynamics Reinforcement Learner
Continuous state spaces and stochastic, switching dynamics characterize a number of rich, realworld domains, such as robot navigation across varying terrain. We describe a reinfor...
Emma Brunskill, Bethany R. Leffler, Lihong Li, Mic...
CVPR
2012
IEEE
12 years 12 months ago
Geodesic flow kernel for unsupervised domain adaptation
In real-world applications of visual recognition, many factors—such as pose, illumination, or image quality—can cause a significant mismatch between the source domain on whic...
Boqing Gong, Yuan Shi, Fei Sha, Kristen Grauman
GECCO
2006
Springer
192views Optimization» more  GECCO 2006»
15 years 1 months ago
Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms
This paper presents a methodology for using heuristic search methods to optimise cancer chemotherapy. Specifically, two evolutionary algorithms - Population Based Incremental Lear...
Andrei Petrovski, Siddhartha Shakya, John A. W. Mc...
ECCV
2004
Springer
15 years 11 months ago
Decision Theoretic Modeling of Human Facial Displays
We present a vision based, adaptive, decision theoretic model of human facial displays in interactions. The model is a partially observable Markov decision process, or POMDP. A POM...
Jesse Hoey, James J. Little