Erik B. Sudderth

Michael C. Hughes, Erik B. Sudderth: Nonparametric discovery of activity patterns from video collections. CVPR Workshops 2012: 25-32

Deqing Sun, Erik B. Sudderth, Michael J. Black: Layered segmentation and optical flow estimation over time. CVPR 2012: 1768-1775

Soumya Ghosh, Erik B. Sudderth: Nonparametric learning for layered segmentation of natural images. CVPR 2012: 2272-2279

Dae Il Kim, Michael C. Hughes, Erik B. Sudderth: The Nonparametric Metadata Dependent Relational Model. ICML 2012

Michael C. Hughes, Emily B. Fox, Erik B. Sudderth: Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data. NIPS 2012: 1304-1312

Soumya Ghosh, Erik B. Sudderth, Matthew Loper, Michael J. Black: From Deformations to Parts: Motion-based Segmentation of 3D Objects. NIPS 2012: 2006-2014

Jason L. Pacheco, Erik B. Sudderth: Minimization of Continuous Bethe Approximations: A Positive Variation. NIPS 2012: 2573-2581

Michael Bryant, Erik B. Sudderth: Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes. NIPS 2012: 2708-2716

Nimar S. Arora, Rodrigo de Salvo Braz, Erik B. Sudderth, Stuart J. Russell: Gibbs Sampling in Open-Universe Stochastic Languages. CoRR abs/1203.3464 (2012)

Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky: Bayesian Nonparametric Inference of Switching Dynamic Linear Models. IEEE Transactions on Signal Processing 59(4): 1569-1585 (2011)

Nimar S. Arora, Stuart J. Russell, Paul Kidwell, Erik B. Sudderth: Global Seismic Monitoring: A Bayesian Approach. AAAI 2011

Soumya Ghosh, Andrei B. Ungureanu, Erik B. Sudderth, David M. Blei: Spatial distance dependent Chinese restaurant processes for image segmentation. NIPS 2011: 1476-1484

Dae Il Kim, Erik B. Sudderth: The Doubly Correlated Nonparametric Topic Model. NIPS 2011: 1980-1988

Erik B. Sudderth, Alexander T. Ihler, Michael Isard, William T. Freeman, Alan S. Willsky: Nonparametric belief propagation. Commun. ACM 53(10): 95-103 (2010)

Nimar S. Arora, Stuart J. Russell, Erik B. Sudderth: Automatic Inference in BLOG. Statistical Relational Artificial Intelligence 2010

Nimar S. Arora, Stuart J. Russell, Paul Kidwell, Erik B. Sudderth: Global seismic monitoring as probabilistic inference. NIPS 2010: 73-81

Deqing Sun, Erik B. Sudderth, Michael J. Black: Layered image motion with explicit occlusions, temporal consistency, and depth ordering. NIPS 2010: 2226-2234

Nimar S. Arora, Rodrigo de Salvo Braz, Erik B. Sudderth, Stuart J. Russell: Gibbs Sampling in Open-Universe Stochastic Languages. UAI 2010: 30-39

Qiang Ji, Jiebo Luo, Dimitris N. Metaxas, Antonio Torralba, Thomas S. Huang, Erik B. Sudderth: Guest Editors' Introduction to the Special Section on Probabilistic Graphical Models. IEEE Trans. Pattern Anal. Mach. Intell. 31(10): 1729-1732 (2009)

Jeremy Schiff, Erik B. Sudderth, Kenneth Y. Goldberg: Nonparametric belief propagation for distributed tracking of robot networks with noisy inter-distance measurements. IROS 2009: 1369-1376

Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky: Sharing Features among Dynamical Systems with Beta Processes. NIPS 2009: 549-557

James A. Hendler, Philipp Cimiano, Dmitri A. Dolgov, Anat Levin, Peter Mika, Brian Milch, Louis-Philippe Morency, Boris Motik, Jennifer Neville, Erik B. Sudderth, Luis von Ahn: AI's 10 to Watch. IEEE Intelligent Systems 23(3): 9-19 (2008)

Erik B. Sudderth, Antonio Torralba, William T. Freeman, Alan S. Willsky: Describing Visual Scenes Using Transformed Objects and Parts. International Journal of Computer Vision 77(1-3): 291-330 (2008)

Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky: An HDP-HMM for systems with state persistence. ICML 2008: 312-319

Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky: Nonparametric Bayesian Learning of Switching Linear Dynamical Systems. NIPS 2008: 457-464

Erik B. Sudderth, Michael I. Jordan: Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes. NIPS 2008: 1585-1592

Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan: Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes. ICCV 2007: 1-8

Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan: Image Denoising with Nonparametric Hidden Markov Trees. ICIP (3) 2007: 121-124

Erik B. Sudderth, Martin J. Wainwright, Alan S. Willsky: Loop Series and Bethe Variational Bounds in Attractive Graphical Models. NIPS 2007

Erik B. Sudderth, Antonio Torralba, William T. Freeman, Alan S. Willsky: Depth from Familiar Objects: A Hierarchical Model for 3D Scenes. CVPR (2) 2006: 2410-2417

Erik B. Sudderth, Antonio Torralba, William T. Freeman, Alan S. Willsky: Learning Hierarchical Models of Scenes, Objects, and Parts. ICCV 2005: 1331-1338

Erik B. Sudderth, Antonio Torralba, William T. Freeman, Alan S. Willsky: Describing Visual Scenes using Transformed Dirichlet Processes. NIPS 2005

Erik B. Sudderth, Martin J. Wainwright, Alan S. Willsky: Embedded trees: estimation of Gaussian Processes on graphs with cycles. IEEE Transactions on Signal Processing 52(11): 3136-3150 (2004)

Erik B. Sudderth, Michael I. Mandel, William T. Freeman, Alan S. Willsky: Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation. NIPS 2004

Erik B. Sudderth, Alexander T. Ihler, William T. Freeman, Alan S. Willsky: Nonparametric Belief Propagation. CVPR (1) 2003: 605-612

Alexander T. Ihler, Erik B. Sudderth, William T. Freeman, Alan S. Willsky: Efficient Multiscale Sampling from Products of Gaussian Mixtures. NIPS 2003

John W. Fisher III, Martin J. Wainwright, Erik B. Sudderth, Alan S. Willsky: Statistical and Information-Theoretic Methods for Self-Organization and Fusion of Multimodal, Networked Sensors. IJHPCA 16(3): 337-353 (2002)

Martin J. Wainwright, Erik B. Sudderth, Alan S. Willsky: Tree-Based Modeling and Estimation of Gaussian Processes on Graphs with Cycles. NIPS 2000: 661-667