Erik B. Sudderth

ORCID

Publication Activity (10 Years)

Years Active: 2000-2024
Publications (10 Years): 34

Top Venues

Massachusetts Institute of Technology, Cambridge, MA, USA (PHD)

Education History

Massachusetts Institute of Technology, Cambridge, MA, USA

PHD

Publications

Ali Younis, Erik B. Sudderth
Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes. CoRR (2024)
Federica Zoe Ricci, Erik B. Sudderth, Jaylen Lee, Megan A. K. Peters, Marina Vannucci, Michele Guindani
Bayesian temporal biclustering with applications to multi-subject neuroscience studies. CoRR (2024)
Sakshi Agarwal, Gabriel Hope, Ali Younis, Erik B. Sudderth
A decoder suffices for query-adaptive variational inference. UAI (2023)
Harry Bendekgey, Gabriel Hope, Erik B. Sudderth
Unbiased Learning of Deep Generative Models with Structured Discrete Representations. CoRR (2023)
Ali Younis, Erik B. Sudderth
Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes. NeurIPS (2023)
Harry Bendekgey, Erik B. Sudderth
Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints. NeurIPS (2021)
Geng Ji, Debora Sujono, Erik B. Sudderth
Marginalized Stochastic Natural Gradients for Black-Box Variational Inference. ICML (2021)
Zhile Ren, Erik B. Sudderth
Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts. IEEE Trans. Pattern Anal. Mach. Intell. 42 (10) (2020)
Gabriel Hope, Madina Abdrakhmanova, Xiaoyin Chen, Michael C. Hughes, Erik B. Sudderth
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints. CoRR (2020)
Zhile Ren, Erik B. Sudderth
Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts. CoRR (2019)
Daeyun Shin, Zhile Ren, Erik B. Sudderth, Charless C. Fowlkes
3D Scene Reconstruction With Multi-Layer Depth and Epipolar Transformers. ICCV (2019)
Geng Ji, Dehua Cheng, Huazhong Ning, Changhe Yuan, Hanning Zhou, Liang Xiong, Erik B. Sudderth
Variational Training for Large-Scale Noisy-OR Bayesian Networks. UAI (2019)
Daeyun Shin, Zhile Ren, Erik B. Sudderth, Charless C. Fowlkes
Multi-layer Depth and Epipolar Feature Transformers for 3D Scene Reconstruction. CVPR Workshops (2019)
Zhile Ren, Orazio Gallo, Deqing Sun, Ming-Hsuan Yang, Erik B. Sudderth, Jan Kautz
A Fusion Approach for Multi-Frame Optical Flow Estimation. WACV (2019)
Daeyun Shin, Zhile Ren, Erik B. Sudderth, Charless C. Fowlkes
Multi-layer Depth and Epipolar Feature Transformers for 3D Scene Reconstruction. CoRR (2019)
Zhile Ren, Orazio Gallo, Deqing Sun, Ming-Hsuan Yang, Erik B. Sudderth, Jan Kautz
A Simple and Effective Fusion Approach for Multi-frame Optical Flow Estimation. ECCV Workshops (6) (2018)
Zhile Ren, Orazio Gallo, Deqing Sun, Ming-Hsuan Yang, Erik B. Sudderth, Jan Kautz
A Fusion Approach for Multi-Frame Optical Flow Estimation. CoRR (2018)
Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez
Semi-Supervised Prediction-Constrained Topic Models. AISTATS (2018)
Zhile Ren, Erik B. Sudderth
3D Object Detection With Latent Support Surfaces. CVPR (2018)
Dae Il Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth
Refinery: An Open Source Topic Modeling Web Platform. J. Mach. Learn. Res. 18 (2017)
Geng Ji, Michael C. Hughes, Erik B. Sudderth
From Patches to Images: A Nonparametric Generative Model. ICML (2017)
Michael C. Hughes, Leah Weiner, Gabriel Hope, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez
Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models. CoRR (2017)
Zhile Ren, Deqing Sun, Jan Kautz, Erik B. Sudderth
Cascaded Scene Flow Prediction Using Semantic Segmentation. 3DV (2017)
Geng Ji, Robert Bamler, Erik B. Sudderth, Stephan Mandt
Bayesian Paragraph Vectors. CoRR (2017)
Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez
Prediction-Constrained Topic Models for Antidepressant Recommendation. CoRR (2017)
Zhile Ren, Deqing Sun, Jan Kautz, Erik B. Sudderth
Cascaded Scene Flow Prediction using Semantic Segmentation. CoRR (2017)
Daniel Milstein, Jason Pacheco, Leigh J. Hochberg, John D. Simeral, Beata Jarosiewicz, Erik B. Sudderth
Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces. NIPS (2017)
Michael C. Hughes, Erik B. Sudderth
Fast Learning of Clusters and Topics via Sparse Posteriors. CoRR (2016)
Zhile Ren, Erik B. Sudderth
Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients. CVPR (2016)
Michael C. Hughes, Dae Il Kim, Erik B. Sudderth
Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process. AISTATS (2015)
Ryan P. Adams, Emily B. Fox, Erik B. Sudderth, Yee Whye Teh
Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics. IEEE Trans. Pattern Anal. Mach. Intell. 37 (2) (2015)
Michael C. Hughes, William T. Stephenson, Erik B. Sudderth
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models. NIPS (2015)
Deqing Sun, Erik B. Sudderth, Hanspeter Pfister
Layered RGBD scene flow estimation. CVPR (2015)
Jason Pacheco, Erik B. Sudderth
Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach. ICML (2015)
Soumya Ghosh, Michalis Raptis, Leonid Sigal, Erik B. Sudderth
Nonparametric Clustering with Distance Dependent Hierarchies. UAI (2014)
Jason Pacheco, Silvia Zuffi, Michael J. Black, Erik B. Sudderth
Preserving Modes and Messages via Diverse Particle Selection. ICML (2014)
Dae Il Kim, Prem Gopalan, David M. Blei, Erik B. Sudderth
Efficient Online Inference for Bayesian Nonparametric Relational Models. NIPS (2013)
Michael C. Hughes, Erik B. Sudderth
Memoized Online Variational Inference for Dirichlet Process Mixture Models. NIPS (2013)
Deqing Sun, Jonas Wulff, Erik B. Sudderth, Hanspeter Pfister, Michael J. Black
A Fully-Connected Layered Model of Foreground and Background Flow. CVPR (2013)
Michael C. Hughes, Emily B. Fox, Erik B. Sudderth
Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data. NIPS (2012)
Soumya Ghosh, Erik B. Sudderth
Nonparametric learning for layered segmentation of natural images. CVPR (2012)
Soumya Ghosh, Erik B. Sudderth, Matthew Loper, Michael J. Black
From Deformations to Parts: Motion-based Segmentation of 3D Objects. NIPS (2012)
Deqing Sun, Erik B. Sudderth, Michael J. Black
Layered segmentation and optical flow estimation over time. CVPR (2012)
Dae Il Kim, Michael C. Hughes, Erik B. Sudderth
The Nonparametric Metadata Dependent Relational Model. ICML (2012)
Jason L. Pacheco, Erik B. Sudderth
Minimization of Continuous Bethe Approximations: A Positive Variation. NIPS (2012)
Michael C. Hughes, Erik B. Sudderth
Nonparametric discovery of activity patterns from video collections. CVPR Workshops (2012)
Jason L. Pacheco, Erik B. Sudderth
Improved variational inference for tracking in clutter. SSP (2012)
Nimar S. Arora, Rodrigo de Salvo Braz, Erik B. Sudderth, Stuart J. Russell
Gibbs Sampling in Open-Universe Stochastic Languages CoRR (2012)
Michael Bryant, Erik B. Sudderth
Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes. NIPS (2012)
Nimar S. Arora, Stuart J. Russell, Paul Kidwell, Erik B. Sudderth
Global Seismic Monitoring: A Bayesian Approach. AAAI (2011)
Dae Il Kim, Erik B. Sudderth
The Doubly Correlated Nonparametric Topic Model. NIPS (2011)
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
Bayesian Nonparametric Inference of Switching Dynamic Linear Models. IEEE Trans. Signal Process. 59 (4) (2011)
Soumya Ghosh, Andrei B. Ungureanu, Erik B. Sudderth, David M. Blei
Spatial distance dependent Chinese restaurant processes for image segmentation. NIPS (2011)
Nimar S. Arora, Rodrigo de Salvo Braz, Erik B. Sudderth, Stuart J. Russell
Gibbs Sampling in Open-Universe Stochastic Languages. UAI (2010)
Erik B. Sudderth, Alexander T. Ihler, Michael Isard, William T. Freeman, Alan S. Willsky
Nonparametric belief propagation. Commun. ACM 53 (10) (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)
Deqing Sun, Erik B. Sudderth, Michael J. Black
Layered image motion with explicit occlusions, temporal consistency, and depth ordering. NIPS (2010)
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
Bayesian Nonparametric Methods for Learning Markov Switching Processes. IEEE Signal Process. Mag. 27 (6) (2010)
Jeremy Schiff, Erik B. Sudderth, Kenneth Y. Goldberg
Nonparametric belief propagation for distributed tracking of robot networks with noisy inter-distance measurements. IROS (2009)
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
Sharing Features among Dynamical Systems with Beta Processes. NIPS (2009)
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) (2009)
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 Intell. Syst. 23 (3) (2008)
Erik B. Sudderth, Antonio Torralba, William T. Freeman, Alan S. Willsky
Describing Visual Scenes Using Transformed Objects and Parts. Int. J. Comput. Vis. 77 (1-3) (2008)
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
An HDP-HMM for systems with state persistence. ICML (2008)
Erik B. Sudderth, Michael I. Jordan
Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes. NIPS (2008)
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems. NIPS (2008)
Erik B. Sudderth, William T. Freeman
Signal and Image Processing with Belief Propagation [DSP Applications]. IEEE Signal Process. Mag. 25 (2) (2008)
Erik B. Sudderth, Martin J. Wainwright, Alan S. Willsky
Loop Series and Bethe Variational Bounds in Attractive Graphical Models. NIPS (2007)
Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan
Image Denoising with Nonparametric Hidden Markov Trees. ICIP (3) (2007)
Emily B. Fox, Erik B. Sudderth, Alan S. Willsky
Hierarchical Dirichlet processes for tracking maneuvering targets. FUSION (2007)
Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan
Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes. ICCV (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)
Erik B. Sudderth, Antonio Torralba, William T. Freeman, Alan S. Willsky
Learning Hierarchical Models of Scenes, Objects, and Parts. ICCV (2005)
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 Trans. Signal Process. 52 (11) (2004)
Erik B. Sudderth, Michael I. Mandel, William T. Freeman, Alan S. Willsky
Visual Hand Tracking Using Nonparametric Belief Propagation. CVPR Workshops (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)
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. Int. J. High Perform. Comput. Appl. 16 (3) (2002)
Martin J. Wainwright, Erik B. Sudderth, Alan S. Willsky
Tree-Based Modeling and Estimation of Gaussian Processes on Graphs with Cycles. NIPS (2000)

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Publication Activity (10 Years)

Top Topics

Top Venues

Erik B. Sudderth