Erik B. Sudderth

发表

Signal and Image Processing with Belief Propagation

William T. Freeman, Erik B. Sudderth, 2008 .

Refinery: An Open Source Topic Modeling Web Platform

Erik B. Sudderth, Michael C. Hughes, Dae Il Kim, 2017, J. Mach. Learn. Res..

Fast Learning of Clusters and Topics via Sparse Posteriors pdf

Erik B. Sudderth, Michael C. Hughes, M. Hughes, 2016, ArXiv.

Hierarchical Dirichlet processes for tracking maneuvering targets

Alan S. Willsky, Emily B. Fox, Erik B. Sudderth, 2007, 2007 10th International Conference on Information Fusion.

Depth from Familiar Objects: A Hierarchical Model for 3D Scenes

Antonio Torralba, William T. Freeman, Alan S. Willsky, 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

IEEE International Conference on Image Processing (ICIP 2007). Volume 3, Sept. 16 2007 - Oct. 19 2007

Michael I. Jordan, Erik B. Sudderth, Jyri Kivinen, 2007 .

NET‐VISA: Network Processing Vertically Integrated Seismic Analysis

Erik B. Sudderth, Stuart Russell, Nimar S. Arora, 2013 .

Sharing Features among Dynamical Systems with Beta Processes

Michael I. Jordan, Alan S. Willsky, Emily B. Fox, 2009, NIPS.

Visual Hand Tracking Using Nonparametric Belief Propagation

Michael I. Mandel, William T. Freeman, Alan S. Willsky, 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

Bayesian Nonparametric Methods for Learning Markov Switching Processes

Michael I. Jordan, Alan S. Willsky, Emily B. Fox, 2010, IEEE Signal Processing Magazine.

Preserving Modes and Messages via Diverse Particle Selection

Michael J. Black, Silvia Zuffi, Erik B. Sudderth, 2014, ICML.

A Simple and Effective Fusion Approach for Multi-frame Optical Flow Estimation

Jan Kautz, Deqing Sun, Ming-Hsuan Yang, 2018, ECCV Workshops.

The Doubly Correlated Nonparametric Topic Model

Erik B. Sudderth, Dae Il Kim, D. Kim, 2011, NIPS.

From Deformations to Parts: Motion-based Segmentation of 3D Objects

Soumya Ghosh, Michael J. Black, Erik B. Sudderth, 2012, NIPS.

Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach

Erik B. Sudderth, Jason Pacheco, 2015, ICML.

The Sticky HDP-HMM: Bayesian Nonparametric Hidden Markov Models with Persistent States

Michael I. Jordan, Emily B. Fox, Erik B. Sudderth, 2009 .

Layered image motion with explicit occlusions, temporal consistency, and depth ordering

Michael J. Black, Deqing Sun, Erik B. Sudderth, 2010, NIPS.

Describing Visual Scenes using Transformed Dirichlet Processes

Antonio Torralba, William T. Freeman, Alan S. Willsky, 2005, NIPS.

Statistical and Information-Theoretic Methods for Self-Organization and Fusion of Multimodal, Networked Sensors

Martin J. Wainwright, John W. Fisher, Alan S. Willsky, 2002, Int. J. High Perform. Comput. Appl..

Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes

Michael I. Jordan, Erik B. Sudderth, 2008, NIPS.

Minimization of Continuous Bethe Approximations: A Positive Variation

Erik B. Sudderth, Jason L. Pacheco, 2012, NIPS.

A Fully-Connected Layered Model of Foreground and Background Flow

Michael J. Black, Deqing Sun, Hanspeter Pfister, 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

A Sticky HDP-HMM With Application to Speaker Diarization

Michael I. Jordan, Alan S. Willsky, Emily B. Fox, 2009, 0905.2592.

Spatial distance dependent Chinese restaurant processes for image segmentation

Soumya Ghosh, David M. Blei, Erik B. Sudderth, 2011, NIPS.

3D Object Detection with Latent Support Surfaces

Erik B. Sudderth, Zhile Ren, Zhile Ren, 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Nonparametric belief propagation for distributed tracking of robot networks with noisy inter-distance measurements

Kenneth Y. Goldberg, Erik B. Sudderth, Jeremy Schiff, 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Global seismic monitoring as probabilistic inference

Stuart J. Russell, Erik B. Sudderth, Nimar S. Arora, 2010, NIPS.

Developing a tempered HDP-HMM for Systems with State Persistence

Michael I. Jordan, Alan S. Willsky, Emily B. Fox, 2007 .

Joint Modeling of Multiple Related Time Series via the Beta Process

Michael I. Jordan, Alan S. Willsky, Emily B. Fox, 2011, 1111.4226.

Semi-Supervised Prediction-Constrained Topic Models

Finale Doshi-Velez, Erik B. Sudderth, Michael C. Hughes, 2018, AISTATS.

AI's 10 to Watch

James A. Hendler, Boris Motik, Jennifer Neville, 2008, IEEE Intelligent Systems.

Prediction-Constrained Topic Models for Antidepressant Recommendation pdf

Finale Doshi-Velez, Erik B. Sudderth, Michael C. Hughes, 2017, ArXiv.

Embedded trees: estimation of Gaussian Processes on graphs with cycles

Martin J. Wainwright, Alan S. Willsky, Erik B. Sudderth, 2004, IEEE Transactions on Signal Processing.

Nonparametric belief propagation

William T. Freeman, Alan S. Willsky, Alexander T. Ihler, 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

Layered segmentation and optical flow estimation over time

Michael J. Black, Deqing Sun, Erik B. Sudderth, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

Layered RGBD scene flow estimation

Deqing Sun, Hanspeter Pfister, Erik B. Sudderth, 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

The Nonparametric Metadata Dependent Relational Model

Erik B. Sudderth, Michael C. Hughes, Dae Il Kim, 2012, ICML.

From Patches to Images: A Nonparametric Generative Model

Erik B. Sudderth, Michael C. Hughes, Geng Ji, 2017, ICML.

Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation

Michael I. Mandel, William T. Freeman, Alan S. Willsky, 2004, NIPS.

3D Scene Reconstruction With Multi-Layer Depth and Epipolar Transformers

Charless C. Fowlkes, Erik B. Sudderth, Daeyun Shin, 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Vertically Integrated Seismological Analysis I : Modeling

Michael I. Jordan, Erik B. Sudderth, Stuart Russell, 2009 .

Nonparametric learning for layered segmentation of natural images

Soumya Ghosh, Erik B. Sudderth, S. Ghosh, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

Gibbs Sampling in Open-Universe Stochastic Languages

Stuart J. Russell, Erik B. Sudderth, Nimar S. Arora, 2010, UAI.

Bayesian Nonparametric Inference of Switching Dynamic Linear Models

Michael I. Jordan, Alan S. Willsky, Emily B. Fox, 2010, IEEE Transactions on Signal Processing.

BAYESIAN TREATY MONITORING: PRELIMINARY REPORT

Erik B. Sudderth, Stuart Russell, Nimar S. Arora, 2011 .

Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics

Yee Whye Teh, Ryan P. Adams, Emily B. Fox, 2015, IEEE Trans. Pattern Anal. Mach. Intell..

Tree-Based Modeling and Estimation of Gaussian Processes on Graphs with Cycles

Martin J. Wainwright, Alan S. Willsky, Erik B. Sudderth, 2000, NIPS.

A Fusion Approach for Multi-Frame Optical Flow Estimation

Jan Kautz, Deqing Sun, Ming-Hsuan Yang, 2018, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models pdf

Finale Doshi-Velez, Erik B. Sudderth, Michael C. Hughes, 2017, ArXiv.

Nonparametric discovery of activity patterns from video collections

Erik B. Sudderth, Michael C. Hughes, 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data

Emily B. Fox, Erik B. Sudderth, Michael C. Hughes, 2012, NIPS.

Major Advances and Emerging Developments of Graphical Models [From the Guest Editors]

Martin J. Wainwright, Michael I. Jordan, Alan S. Willsky, 2010 .

Global Seismic Monitoring: A Bayesian Approach

Stuart J. Russell, Erik B. Sudderth, Nimar S. Arora, 2011, AAAI.

Variational Training for Large-Scale Noisy-OR Bayesian Networks

Changhe Yuan, Hanning Zhou, Liang Xiong, 2019, UAI.

JOINT MODELING OF MULTIPLE TIME SERIES VIA THE BETA PROCESS WITH APPLICATION TO MOTION CAPTURE SEGMENTATION

Michael I. Jordan, Emily B. Fox, Erik B. Sudderth, 2013, 1308.4747.

Nonparametric Clustering with Distance Dependent Hierarchies

Soumya Ghosh, Leonid Sigal, Erik B. Sudderth, 2014, UAI.

Efficient Online Inference for Bayesian Nonparametric Relational Models

David M. Blei, Erik B. Sudderth, Dae Il Kim, 2013, NIPS.

Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes

Erik B. Sudderth, Michael Bryant, Michael Bryant, 2012, NIPS.

Multi-layer Depth and Epipolar Feature Transformers for 3D Scene Reconstruction

Charless C. Fowlkes, Erik B. Sudderth, Daeyun Shin, 2019, CVPR Workshops.

Nonparametric Bayesian Learning of Switching Linear Dynamical Systems

Michael I. Jordan, Alan S. Willsky, Emily B. Fox, 2008, NIPS.

Learning hierarchical models of scenes, objects, and parts

Antonio Torralba, William T. Freeman, Alan S. Willsky, 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

Guest Editors' Introduction to the Special Section on Probabilistic Graphical Models

Jiebo Luo, Qiang Ji, Thomas S. Huang, 2009, IEEE Trans. Pattern Anal. Mach. Intell..

Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients

Erik B. Sudderth, Zhile Ren, Zhile Ren, 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces

John D. Simeral, Erik B. Sudderth, Beata Jarosiewicz, 2017, NIPS.

Graphical models for visual object recognition and tracking

Erik B. Sudderth, 2006 .

Bayesian Paragraph Vectors pdf

Erik B. Sudderth, Stephan Mandt, Robert Bamler, 2017, ArXiv.

Nonparametric Bayesian Identification of Jump Systems with Sparse Dependencies

Michael I. Jordan, Alan S. Willsky, Emily B. Fox, 2009 .

Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints pdf

Erik B. Sudderth, Michael C. Hughes, Xiaoyin Chen, 2020, ArXiv.

Automatic Inference in BLOG

Stuart J. Russell, Erik B. Sudderth, Nimar S. Arora, 2010, StarAI@AAAI.

Describing Visual Scenes Using Transformed Objects and Parts

Antonio Torralba, William T. Freeman, Alan S. Willsky, 2008, International Journal of Computer Vision.

Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes

Michael I. Jordan, Erik B. Sudderth, Jyri J. Kivinen, 2007, 2007 IEEE 11th International Conference on Computer Vision.

Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process

Erik B. Sudderth, Michael C. Hughes, Dae Il Kim, 2015, AISTATS.

Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models

Erik B. Sudderth, William T. Stephenson, Michael C. Hughes, 2015, NIPS.

Improved variational inference for tracking in clutter

Erik B. Sudderth, Jason L. Pacheco, Jason L. Pacheco, 2012, 2012 IEEE Statistical Signal Processing Workshop (SSP).

Memoized Online Variational Inference for Dirichlet Process Mixture Models

Erik B. Sudderth, Michael C. Hughes, M. Hughes, 2013, NIPS.

Efficient Multiscale Sampling from Products of Gaussian Mixtures

William T. Freeman, Alan S. Willsky, Alexander T. Ihler, 2003, NIPS.

Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts

Erik B. Sudderth, Zhile Ren, Zhile Ren, 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Image Denoising with Nonparametric Hidden Markov Trees

Michael I. Jordan, Erik B. Sudderth, Jyri J. Kivinen, 2007, 2007 IEEE International Conference on Image Processing.

Marginalized Stochastic Natural Gradients for Black-Box Variational Inference

Erik B. Sudderth, Debora Sujono, Geng Ji, 2021, ICML.

Loop Series and Bethe Variational Bounds in Attractive Graphical Models

Erik B. Sudderth, A. Willsky, M. Wainwright, 2007, NIPS.

An HDP-HMM for systems with state persistence

Michael I. Jordan, Erik B. Sudderth, Alan S Willsky, 2008, ICML '08.

Introduction to statistical machine learning

Erik B. Sudderth, 2016 .

Nonparametric Belief Propagation and Facial Appearance Estimation

Erik B. Sudderth, W. Freeman, A. Willsky, 2002 .

Annual grassland resource pools and fluxes: sensitivity to precipitation and dry periods on two contrasting soils

Erik B. Sudderth, Erika A. Sudderth, G. Andersen, 2012 .

A spectral clustering search algorithm for predicting shallow landslide size and location

Erik B. Sudderth, W. Dietrich, D. Bellugi, 2015, Journal of Geophysical Research: Earth Surface.

Supplement : Semi-Supervised Prediction-Constrained Topic Models

Erik B. Sudderth, Michael C. Hughes, Finale Doshi-Velez, 2018 .

Nonparametric Bayesian Identification of Jump Systems with Sparse Dependencies

Michael I. Jordan, Alan S. Willsky, Emily B. Fox, 2009 .

Eleventh IEEE International Conference on Computer Vision (ICCV 2007), Rio de Janeiro, Brazil, October 14-20, 2007

Erik B. Sudderth, Jyri Kivinen, Michael I. Jordan, 2007 .

6.962 Week 6 Summary: Approximate Inference Techniques for Graphs with Cycles

Erik B. Sudderth, 2022 .

From Patches to Natural Images via Hierarchical Dirichlet Processes

Erik B. Sudderth, Michael C. Hughes, Geng Ji, 2016 .

Variational Inference for Hierarchical Dirichlet Process Based Nonparametric Models

Erik B. Sudderth, 2015 .

Vertically Integrated Seismological Analysis II : Inference

Erik B. Sudderth, Nimar S. Arora, S. Russell, 2009 .

Quantifying aphid behavioral responses to environmental change

Erik B. Sudderth, Erika A. Sudderth, 2014 .

4-2011 Bayesian Nonparametric Inference of Switching Linear Dynamical Systems

Michael I. Jordan, Erik B. Sudderth, E. Fox, 2017 .

Variational Inference for Soil Biogeochemical Models

Erik B. Sudderth, S. Allison, Hua W. Xie, 2022 .

Prediction-Constrained Hidden Markov Models for Semi-Supervised Classiﬁcation

Erik B. Sudderth, Finale Doshi-Velez, Gabriel Hope, 2021 .

JOINT MODELING OF MULTIPLE TIME SERIES VIA THE BETA PROCESS WITH APPLICATION TO MOTION CAPTURE SEGMENTATION

Michael I. Jordan, Erik B. Sudderth, E. Fox, 2013, 1308.4747.

Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints

Erik B. Sudderth, H. Bendekgey, 2021, NeurIPS.

Approximate Bayesian Computation for Distance-Dependent Learning

Erik B. Sudderth, S. Ghosh, 2015 .

Unbiased Learning of Deep Generative Models with Structured Discrete Representations

Erik B. Sudderth, Gabriel Hope, H. Bendekgey, 2023, arXiv.org.

Embedded trees: estimation of Gaussian Processes on graphs with cycles

Erik B. Sudderth, A. Willsky, M. Wainwright, 2004, IEEE Transactions on Signal Processing.

Prediction-Constrained Markov Models for Medical Time Series with Missing Data and Few Labels

Erik B. Sudderth, Gabriel Hope, Kyle Heuton, 2022 .

Variation in soil moisture and N availability modulates carbon and water exchange in a California grassland experiment

Erik B. Sudderth, Torn, D. Eggett, 2009 .

Joint probabilistic detection , association , & localization I : Hierarchical modeling

Michael I. Jordan, Erik B. Sudderth, Nimar S. Arora, 2009 .

Embedded Trees : Estimation of Gaussian Processes on Graphs with Cycles

Erik B. Sudderth, A. Willsky, M. Wainwright, 2002 .

Graphical models and message-passing algorithms for network-constrained decision problems

Greg, Jason K. Johnson, Wee Peng Tay, 2008 .

Deep Recurrent and Convolutional Neural Networks for Automated Behavior Classification

Erik B. Sudderth, 2016 .

Describing Visual Scenes Using Transformed Objects and Parts

Erik B. Sudderth, A. Torralba, W. Freeman, 2008, International Journal of Computer Vision.

A decoder suffices for query-adaptive variational inference

Erik B. Sudderth, Gabriel Hope, Sakshi Agarwal, 2023, Conference on Uncertainty in Artificial Intelligence.

A framework for variational inference and data assimilation of soil biogeochemical models using state space approximations and normalizing ﬂows

Erik B. Sudderth, Debora Sujono, Hua Wally Xie, 2022 .

Bayesian temporal biclustering with applications to multi-subject neuroscience studies

Erik B. Sudderth, Marina Vannucci, Federica Zoe Ricci, 2024, arXiv.org.