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Erik B. Sudderth
Person information
- affiliation: University of California, Irvine, CA, USA
- affiliation (former): Brown University, Providence, RI, USA
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2020 – today
- 2024
- [i13]Ali Younis, Erik B. Sudderth:
Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes. CoRR abs/2404.08789 (2024) - [i12]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 abs/2406.17131 (2024) - 2023
- [c59]Henry C. Bendekgey, Gabe Hope, Erik B. Sudderth:
Unbiased learning of deep generative models with structured discrete representations. NeurIPS 2023 - [c58]Ali Younis, Erik B. Sudderth:
Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes. NeurIPS 2023 - [c57]Sakshi Agarwal, Gabriel Hope, Ali Younis, Erik B. Sudderth:
A decoder suffices for query-adaptive variational inference. UAI 2023: 33-44 - [i11]Harry Bendekgey
, Gabriel Hope, Erik B. Sudderth:
Unbiased Learning of Deep Generative Models with Structured Discrete Representations. CoRR abs/2306.08230 (2023) - 2022
- [c56]Federica Zoe Ricci, Michele Guindani, Erik B. Sudderth:
Thinned random measures for sparse graphs with overlapping communities. NeurIPS 2022 - 2021
- [c55]Geng Ji, Debora Sujono, Erik B. Sudderth:
Marginalized Stochastic Natural Gradients for Black-Box Variational Inference. ICML 2021: 4870-4881 - [c54]Harry Bendekgey, Erik B. Sudderth:
Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints. NeurIPS 2021: 30023-30036 - 2020
- [j12]Zhile Ren
Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts. IEEE Trans. Pattern Anal. Mach. Intell. 42(10): 2670-2683 (2020) - [i10]Gabriel Hope, Madina Abdrakhmanova, Xiaoyin Chen, Michael C. Hughes, Erik B. Sudderth:
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints. CoRR abs/2012.06718 (2020)
2010 – 2019
- 2019
- [c53]Daeyun Shin, Zhile Ren, Erik B. Sudderth, Charless C. Fowlkes:
Multi-layer Depth and Epipolar Feature Transformers for 3D Scene Reconstruction. CVPR Workshops 2019: 39-43 - [c52]Daeyun Shin, Zhile Ren, Erik B. Sudderth
3D Scene Reconstruction With Multi-Layer Depth and Epipolar Transformers. ICCV 2019: 2172-2182 - [c51]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: 873-882 - [c50]Zhile Ren, Orazio Gallo, Deqing Sun, Ming-Hsuan Yang, Erik B. Sudderth
A Fusion Approach for Multi-Frame Optical Flow Estimation. WACV 2019: 2077-2086 - [i9]Daeyun Shin, Zhile Ren, Erik B. Sudderth, Charless C. Fowlkes:
Multi-layer Depth and Epipolar Feature Transformers for 3D Scene Reconstruction. CoRR abs/1902.06729 (2019) - [i8]Zhile Ren, Erik B. Sudderth:
Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts. CoRR abs/1906.04725 (2019) - 2018
- [c49]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: 1067-1076 - [c48]Zhile Ren, Erik B. Sudderth
3D Object Detection With Latent Support Surfaces. CVPR 2018: 937-946 - [c47]Zhile Ren, Orazio Gallo, Deqing Sun, Ming-Hsuan Yang, Erik B. Sudderth
A Simple and Effective Fusion Approach for Multi-frame Optical Flow Estimation. ECCV Workshops (6) 2018: 706-710 - [i7]Zhile Ren, Orazio Gallo, Deqing Sun, Ming-Hsuan Yang, Erik B. Sudderth, Jan Kautz:
A Fusion Approach for Multi-Frame Optical Flow Estimation. CoRR abs/1810.10066 (2018) - 2017
- [j11]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: 12:1-12:5 (2017) - [c46]Zhile Ren, Deqing Sun, Jan Kautz, Erik B. Sudderth
Cascaded Scene Flow Prediction Using Semantic Segmentation. 3DV 2017: 225-233 - [c45]Geng Ji, Michael C. Hughes
From Patches to Images: A Nonparametric Generative Model. ICML 2017: 1675-1683 - [c44]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: 868-878 - [i6]Michael C. Hughes
Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models. CoRR abs/1707.07341 (2017) - [i5]Zhile Ren, Deqing Sun, Jan Kautz, Erik B. Sudderth:
Cascaded Scene Flow Prediction using Semantic Segmentation. CoRR abs/1707.08313 (2017) - [i4]Geng Ji, Robert Bamler
Bayesian Paragraph Vectors. CoRR abs/1711.03946 (2017) - [i3]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 abs/1712.00499 (2017) - 2016
- [c43]Zhile Ren, Erik B. Sudderth
Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients. CVPR 2016: 1525-1533 - [i2]Michael C. Hughes
Fast Learning of Clusters and Topics via Sparse Posteriors. CoRR abs/1609.07521 (2016) - 2015
- [j10]Ryan P. Adams, Emily B. Fox
Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 209-211 (2015) - [c42]Michael C. Hughes
Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process. AISTATS 2015 - [c41]Deqing Sun, Erik B. Sudderth
Layered RGBD scene flow estimation. CVPR 2015: 548-556 - [c40]Jason Pacheco, Erik B. Sudderth:
Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach. ICML 2015: 2200-2208 - [c39]Michael C. Hughes, William T. Stephenson, Erik B. Sudderth:
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models. NIPS 2015: 1198-1206 - 2014
- [c38]Jason Pacheco, Silvia Zuffi, Michael J. Black, Erik B. Sudderth:
Preserving Modes and Messages via Diverse Particle Selection. ICML 2014: 1152-1160 - [c37]Soumya Ghosh, Michalis Raptis, Leonid Sigal, Erik B. Sudderth:
Nonparametric Clustering with Distance Dependent Hierarchies. UAI 2014: 260-269 - 2013
- [c36]Deqing Sun, Jonas Wulff, Erik B. Sudderth
A Fully-Connected Layered Model of Foreground and Background Flow. CVPR 2013: 2451-2458 - [c35]Dae Il Kim, Prem Gopalan, David M. Blei, Erik B. Sudderth:
Efficient Online Inference for Bayesian Nonparametric Relational Models. NIPS 2013: 962-970 - [c34]Michael C. Hughes, Erik B. Sudderth:
Memoized Online Variational Inference for Dirichlet Process Mixture Models. NIPS 2013: 1133-1141 - 2012
- [c33]Michael C. Hughes, Erik B. Sudderth
Nonparametric discovery of activity patterns from video collections. CVPR Workshops 2012: 25-32 - [c32]Deqing Sun, Erik B. Sudderth
Layered segmentation and optical flow estimation over time. CVPR 2012: 1768-1775 - [c31]Soumya Ghosh, Erik B. Sudderth
Nonparametric learning for layered segmentation of natural images. CVPR 2012: 2272-2279 - [c30]Dae Il Kim, Michael C. Hughes, Erik B. Sudderth:
The Nonparametric Metadata Dependent Relational Model. ICML 2012 - [c29]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 - [c28]Soumya Ghosh, Erik B. Sudderth, Matthew Loper, Michael J. Black:
From Deformations to Parts: Motion-based Segmentation of 3D Objects. NIPS 2012: 2006-2014 - [c27]Jason L. Pacheco, Erik B. Sudderth:
Minimization of Continuous Bethe Approximations: A Positive Variation. NIPS 2012: 2573-2581 - [c26]Michael Bryant, Erik B. Sudderth:
Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes. NIPS 2012: 2708-2716 - [c25]Jason L. Pacheco, Erik B. Sudderth
Improved variational inference for tracking in clutter. SSP 2012: 852-855 - [i1]Nimar S. Arora, Rodrigo de Salvo Braz, Erik B. Sudderth, Stuart Russell:
Gibbs Sampling in Open-Universe Stochastic Languages. CoRR abs/1203.3464 (2012) - 2011
- [j9]Emily B. Fox
Bayesian Nonparametric Inference of Switching Dynamic Linear Models. IEEE Trans. Signal Process. 59(4): 1569-1585 (2011) - [c24]Nimar S. Arora, Stuart Russell, Paul Kidwell, Erik B. Sudderth:
Global Seismic Monitoring: A Bayesian Approach. AAAI 2011: 1533-1536 - [c23]Soumya Ghosh, Andrei B. Ungureanu, Erik B. Sudderth, David M. Blei:
Spatial distance dependent Chinese restaurant processes for image segmentation. NIPS 2011: 1476-1484 - [c22]Dae Il Kim, Erik B. Sudderth:
The Doubly Correlated Nonparametric Topic Model. NIPS 2011: 1980-1988 - 2010
- [j8]Erik B. Sudderth
Nonparametric belief propagation. Commun. ACM 53(10): 95-103 (2010) - [j7]Emily B. Fox
Bayesian Nonparametric Methods for Learning Markov Switching Processes. IEEE Signal Process. Mag. 27(6): 43-54 (2010) - [c21]Nimar S. Arora, Stuart Russell, Erik B. Sudderth:
Automatic Inference in BLOG. StarAI@AAAI 2010 - [c20]Nimar S. Arora, Stuart Russell, Paul Kidwell, Erik B. Sudderth:
Global seismic monitoring as probabilistic inference. NIPS 2010: 73-81 - [c19]Deqing Sun, Erik B. Sudderth, Michael J. Black:
Layered image motion with explicit occlusions, temporal consistency, and depth ordering. NIPS 2010: 2226-2234 - [c18]Nimar S. Arora, Rodrigo de Salvo Braz, Erik B. Sudderth, Stuart Russell:
Gibbs Sampling in Open-Universe Stochastic Languages. UAI 2010: 30-39
2000 – 2009
- 2009
- [j6]Qiang Ji, Jiebo Luo
Guest Editors' Introduction to the Special Section on Probabilistic Graphical Models. IEEE Trans. Pattern Anal. Mach. Intell. 31(10): 1729-1732 (2009) - [c17]Jeremy Schiff, Erik B. Sudderth
Nonparametric belief propagation for distributed tracking of robot networks with noisy inter-distance measurements. IROS 2009: 1369-1376 - [c16]Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky:
Sharing Features among Dynamical Systems with Beta Processes. NIPS 2009: 549-557 - 2008
- [j5]James A. Hendler
AI's 10 to Watch. IEEE Intell. Syst. 23(3): 9-19 (2008) - [j4]Erik B. Sudderth
Describing Visual Scenes Using Transformed Objects and Parts. Int. J. Comput. Vis. 77(1-3): 291-330 (2008) - [j3]Erik B. Sudderth
Signal and Image Processing with Belief Propagation [DSP Applications]. IEEE Signal Process. Mag. 25(2): 114-141 (2008) - [c15]Emily B. Fox, Erik B. Sudderth
An HDP-HMM for systems with state persistence. ICML 2008: 312-319 - [c14]Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky:
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems. NIPS 2008: 457-464 - [c13]Erik B. Sudderth, Michael I. Jordan:
Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes. NIPS 2008: 1585-1592 - 2007
- [c12]Emily B. Fox, Erik B. Sudderth
Hierarchical Dirichlet processes for tracking maneuvering targets. FUSION 2007: 1-8 - [c11]Jyri J. Kivinen, Erik B. Sudderth
Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes. ICCV 2007: 1-8 - [c10]Jyri J. Kivinen, Erik B. Sudderth
Image Denoising with Nonparametric Hidden Markov Trees. ICIP (3) 2007: 121-124 - [c9]Erik B. Sudderth, Martin J. Wainwright, Alan S. Willsky:
Loop Series and Bethe Variational Bounds in Attractive Graphical Models. NIPS 2007: 1425-1432 - 2006
- [b1]Erik B. Sudderth:
Graphical models for visual object recognition and tracking. Massachusetts Institute of Technology, Cambridge, MA, USA, 2006 - [c8]Erik B. Sudderth
Depth from Familiar Objects: A Hierarchical Model for 3D Scenes. CVPR (2) 2006: 2410-2417 - 2005
- [c7]Erik B. Sudderth
Learning Hierarchical Models of Scenes, Objects, and Parts. ICCV 2005: 1331-1338 - [c6]Erik B. Sudderth, Antonio Torralba, William T. Freeman, Alan S. Willsky:
Describing Visual Scenes using Transformed Dirichlet Processes. NIPS 2005: 1297-1304 - 2004
- [j2]Erik B. Sudderth
Embedded trees: estimation of Gaussian Processes on graphs with cycles. IEEE Trans. Signal Process. 52(11): 3136-3150 (2004) - [c5]Erik B. Sudderth
Visual Hand Tracking Using Nonparametric Belief Propagation. CVPR Workshops 2004: 189 - [c4]Erik B. Sudderth, Michael I. Mandel, William T. Freeman, Alan S. Willsky:
Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation. NIPS 2004: 1369-1376 - 2003
- [c3]Erik B. Sudderth, Alexander T. Ihler, William T. Freeman, Alan S. Willsky:
Nonparametric Belief Propagation. CVPR (1) 2003: 605-612 - [c2]Alexander T. Ihler, Erik B. Sudderth, William T. Freeman, Alan S. Willsky:
Efficient Multiscale Sampling from Products of Gaussian Mixtures. NIPS 2003: 1-8 - 2002
- [j1]John W. Fisher III
Statistical and Information-Theoretic Methods for Self-Organization and Fusion of Multimodal, Networked Sensors. Int. J. High Perform. Comput. Appl. 16(3): 337-353 (2002) - 2000
- [c1]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
Coauthor Index
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