Παρακολούθηση
Erik B. Sudderth
Erik B. Sudderth
Professor of Computer Science, UC Irvine
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα uci.edu - Αρχική σελίδα
Τίτλος
Παρατίθεται από
Παρατίθεται από
Έτος
Nonparametric belief propagation
EB Sudderth, AT Ihler, WT Freeman, AS Willsky
IEEE Conference on Computer Vision & Pattern Recognition, 605-612, 2003
586*2003
A sticky HDP-HMM with application to speaker diarization
EB Fox, EB Sudderth, MI Jordan, AS Willsky
The Annals of Applied Statistics, 1020-1056, 2011
531*2011
Learning hierarchical models of scenes, objects, and parts
EB Sudderth, A Torralba, WT Freeman, AS Willsky
Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 2 …, 2005
4282005
An HDP-HMM for systems with state persistence
EB Fox, EB Sudderth, MI Jordan, AS Willsky
Proceedings of the 25th international conference on Machine learning, 312-319, 2008
3402008
Nonparametric belief propagation
EB Sudderth, AT Ihler, M Isard, WT Freeman, AS Willsky
Communications of the ACM 53 (10), 95-103, 2010
3182010
Nonparametric Bayesian learning of switching linear dynamical systems
E Fox, E Sudderth, M Jordan, A Willsky
Advances in neural information processing systems 21, 2008
2512008
Bayesian nonparametric inference of switching dynamic linear models
E Fox, EB Sudderth, MI Jordan, AS Willsky
IEEE Transactions on Signal Processing 59 (4), 1569-1585, 2011
2502011
Visual hand tracking using nonparametric belief propagation
EB Sudderth, MI Mandel, WT Freeman, AS Willsky
2004 Conference on Computer Vision and Pattern Recognition Workshop, 189-189, 2004
2472004
Graphical models for visual object recognition and tracking
EB Sudderth
Massachusetts Institute of Technology, 2006
2412006
Describing visual scenes using transformed objects and parts
EB Sudderth, A Torralba, WT Freeman, AS Willsky
International Journal of Computer Vision 77 (1), 291-330, 2008
2402008
Shared segmentation of natural scenes using dependent Pitman-Yor processes
E Sudderth, M Jordan
Advances in neural information processing systems 21, 2008
2242008
Describing visual scenes using transformed dirichlet processes
A Torralba, A Willsky, E Sudderth, W Freeman
Advances in neural information processing systems 18, 2005
1862005
Sharing features among dynamical systems with beta processes
E Fox, M Jordan, E Sudderth, A Willsky
Advances in neural information processing systems 22, 2009
1732009
Layered image motion with explicit occlusions, temporal consistency, and depth ordering
D Sun, E Sudderth, M Black
Advances in Neural Information Processing Systems 23, 2010
1502010
Layered image motion with explicit occlusions, temporal consistency, and depth ordering
D Sun, E Sudderth, M Black
Advances in Neural Information Processing Systems 23, 2010
1502010
Layered segmentation and optical flow estimation over time
D Sun, EB Sudderth, MJ Black
2012 IEEE Conference on Computer Vision and Pattern Recognition, 1768-1775, 2012
1452012
Three-dimensional object detection and layout prediction using clouds of oriented gradients
Z Ren, EB Sudderth
Proceedings of the IEEE conference on computer vision and pattern …, 2016
1422016
Memoized online variational inference for Dirichlet process mixture models
MC Hughes, E Sudderth
Advances in neural information processing systems 26, 2013
1152013
Joint modeling of multiple time series via the beta process with application to motion capture segmentation
EB Fox, MC Hughes, EB Sudderth, MI Jordan
The Annals of Applied Statistics 8 (3), 1281-1313, 2014
1102014
Efficient multiscale sampling from products of Gaussian mixtures
A Ihler, E Sudderth, W Freeman, A Willsky
Advances in Neural Information Processing Systems 16, 2003
1102003
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