Erik B. Sudderth

Orcid: 0000-0002-0595-9726

Affiliations:
  • University of California, Irvine, CA, USA
  • Brown University, Providence, RI, USA (former)


According to our database1, Erik B. Sudderth authored at least 79 papers between 2000 and 2024.

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Bibliography

2024
Bayesian temporal biclustering with applications to multi-subject neuroscience studies.
CoRR, 2024

2023
Unbiased Learning of Deep Generative Models with Structured Discrete Representations.
CoRR, 2023

A decoder suffices for query-adaptive variational inference.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Unbiased learning of deep generative models with structured discrete representations.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Thinned random measures for sparse graphs with overlapping communities.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Marginalized Stochastic Natural Gradients for Black-Box Variational Inference.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts.
IEEE Trans. Pattern Anal. Mach. Intell., 2020

Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints.
CoRR, 2020

2019
A Fusion Approach for Multi-Frame Optical Flow Estimation.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2019

Variational Training for Large-Scale Noisy-OR Bayesian Networks.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

3D Scene Reconstruction With Multi-Layer Depth and Epipolar Transformers.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Multi-layer Depth and Epipolar Feature Transformers for 3D Scene Reconstruction.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019

2018
A Simple and Effective Fusion Approach for Multi-frame Optical Flow Estimation.
Proceedings of the Computer Vision - ECCV 2018 Workshops, 2018

3D Object Detection With Latent Support Surfaces.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Semi-Supervised Prediction-Constrained Topic Models.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Refinery: An Open Source Topic Modeling Web Platform.
J. Mach. Learn. Res., 2017

Prediction-Constrained Topic Models for Antidepressant Recommendation.
CoRR, 2017

Bayesian Paragraph Vectors.
CoRR, 2017

Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models.
CoRR, 2017

Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

From Patches to Images: A Nonparametric Generative Model.
Proceedings of the 34th International Conference on Machine Learning, 2017

Cascaded Scene Flow Prediction Using Semantic Segmentation.
Proceedings of the 2017 International Conference on 3D Vision, 2017

2016
Fast Learning of Clusters and Topics via Sparse Posteriors.
CoRR, 2016

Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

2015
Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Layered RGBD scene flow estimation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
Nonparametric Clustering with Distance Dependent Hierarchies.
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Preserving Modes and Messages via Diverse Particle Selection.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Efficient Online Inference for Bayesian Nonparametric Relational Models.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Memoized Online Variational Inference for Dirichlet Process Mixture Models.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

A Fully-Connected Layered Model of Foreground and Background Flow.
Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013

2012
Improved variational inference for tracking in clutter.
Proceedings of the IEEE Statistical Signal Processing Workshop, 2012

Minimization of Continuous Bethe Approximations: A Positive Variation.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

From Deformations to Parts: Motion-based Segmentation of 3D Objects.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

The Nonparametric Metadata Dependent Relational Model.
Proceedings of the 29th International Conference on Machine Learning, 2012

Layered segmentation and optical flow estimation over time.
Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

Nonparametric discovery of activity patterns from video collections.
Proceedings of the 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012

Nonparametric learning for layered segmentation of natural images.
Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

2011
Bayesian Nonparametric Inference of Switching Dynamic Linear Models.
IEEE Trans. Signal Process., 2011

The Doubly Correlated Nonparametric Topic Model.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Spatial distance dependent Chinese restaurant processes for image segmentation.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Global Seismic Monitoring: A Bayesian Approach.
Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011

2010
Bayesian Nonparametric Methods for Learning Markov Switching Processes.
IEEE Signal Process. Mag., 2010

Nonparametric belief propagation.
Commun. ACM, 2010

Gibbs Sampling in Open-Universe Stochastic Languages.
Proceedings of the UAI 2010, 2010

Layered image motion with explicit occlusions, temporal consistency, and depth ordering.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Global seismic monitoring as probabilistic inference.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Automatic Inference in BLOG.
Proceedings of the Statistical Relational Artificial Intelligence, 2010

2009
Guest Editors' Introduction to the Special Section on Probabilistic Graphical Models.
IEEE Trans. Pattern Anal. Mach. Intell., 2009

Sharing Features among Dynamical Systems with Beta Processes.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Nonparametric belief propagation for distributed tracking of robot networks with noisy inter-distance measurements.
Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009

2008
Signal and Image Processing with Belief Propagation [DSP Applications].
IEEE Signal Process. Mag., 2008

Describing Visual Scenes Using Transformed Objects and Parts.
Int. J. Comput. Vis., 2008

AI's 10 to Watch.
IEEE Intell. Syst., 2008

Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Nonparametric Bayesian Learning of Switching Linear Dynamical Systems.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

An HDP-HMM for systems with state persistence.
Proceedings of the Machine Learning, 2008

2007
Loop Series and Bethe Variational Bounds in Attractive Graphical Models.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Image Denoising with Nonparametric Hidden Markov Trees.
Proceedings of the International Conference on Image Processing, 2007

Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes.
Proceedings of the IEEE 11th International Conference on Computer Vision, 2007

Hierarchical Dirichlet processes for tracking maneuvering targets.
Proceedings of the 10th International Conference on Information Fusion, 2007

2006
Graphical models for visual object recognition and tracking.
PhD thesis, 2006

Depth from Familiar Objects: A Hierarchical Model for 3D Scenes.
Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), 2006

2005
Describing Visual Scenes using Transformed Dirichlet Processes.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Learning Hierarchical Models of Scenes, Objects, and Parts.
Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV 2005), 2005

2004
Embedded trees: estimation of Gaussian Processes on graphs with cycles.
IEEE Trans. Signal Process., 2004

Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Visual Hand Tracking Using Nonparametric Belief Propagation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2004

2003
Efficient Multiscale Sampling from Products of Gaussian Mixtures.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Nonparametric Belief Propagation.
Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2003), 2003

2002
Statistical and Information-Theoretic Methods for Self-Organization and Fusion of Multimodal, Networked Sensors.
Int. J. High Perform. Comput. Appl., 2002

2000
Tree-Based Modeling and Estimation of Gaussian Processes on Graphs with Cycles.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000


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