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141
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CVPR
2011
IEEE
14 years 12 months ago
Extracting and Locating Temporal Motifs in Video Scenes Using a Hierarchical Non Parametric Bayesian Model
In this paper, we present an unsupervised method for mining activities in videos. From unlabeled video sequences of a scene, our method can automatically recover what are the recu...
Ré, mi Emonet, Jagannadan Varadarajan, Jean-Marc ...
190
Voted
AROBOTS
2011
14 years 10 months ago
Learning GP-BayesFilters via Gaussian process latent variable models
Abstract— GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filt...
Jonathan Ko, Dieter Fox
177
Voted
CVPR
2012
IEEE
13 years 6 months ago
Bridging the past, present and future: Modeling scene activities from event relationships and global rules
This paper addresses the discovery of activities and learns the underlying processes that govern their occurrences over time in complex surveillance scenes. To this end, we propos...
Jagannadan Varadarajan, Rémi Emonet, Jean-M...
162
Voted
PE
2010
Springer
212views Optimization» more  PE 2010»
14 years 10 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...
180
Voted
ML
2002
ACM
178views Machine Learning» more  ML 2002»
15 years 3 months ago
Metric-Based Methods for Adaptive Model Selection and Regularization
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Dale Schuurmans, Finnegan Southey