Sudderth, Erik B.

51 publications

ICLRW 2025 VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference Sakshi Agarwal, Gabriel Hope, Erik B. Sudderth

NeurIPS 2024 Learning to Be Smooth: An End-to-End Differentiable Particle Smoother Ali Younis, Erik B. Sudderth

UAI 2023 A Decoder Suffices for Query-Adaptive Variational Inference Sakshi Agarwal, Gabriel Hope, Ali Younis, Erik B. Sudderth

NeurIPS 2023 Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes Ali Younis, Erik B. Sudderth

NeurIPS 2023 Unbiased Learning of Deep Generative Models with Structured Discrete Representations Henry C Bendekgey, Gabe Hope, Erik B. Sudderth

NeurIPSW 2022 Prediction-Constrained Markov Models for Medical Time Series with Missing Data and Few Labels Preetish Rath, Gabriel Hope, Kyle Heuton, Erik B. Sudderth, Michael C Hughes

NeurIPS 2022 Thinned Random Measures for Sparse Graphs with Overlapping Communities Federica Zoe Ricci, Michele Guindani, Erik B. Sudderth

ICMLW 2022 Variational Inference for Soil Biogeochemical Models Debora Sujono, Hua Wally Xie, Steven Allison, Erik B. Sudderth

ICML 2021 Marginalized Stochastic Natural Gradients for Black-Box Variational Inference Geng Ji, Debora Sujono, Erik B Sudderth

NeurIPS 2021 Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints Henry C Bendekgey, Erik B. Sudderth

WACV 2019 A Fusion Approach for Multi-Frame Optical Flow Estimation Zhile Ren, Orazio Gallo, Deqing Sun, Ming-Hsuan Yang, Erik B. Sudderth, Jan Kautz

CVPRW 2019 Multi-Layer Depth and Epipolar Feature Transformers for 3D Scene Reconstruction Daeyun Shin, Zhile Ren, Erik B. Sudderth, Charless C. Fowlkes

UAI 2019 Variational Training for Large-Scale Noisy-or Bayesian Networks Geng Ji, Dehua Cheng, Huazhong Ning, Changhe Yuan, Hanning Zhou, Liang Xiong, Erik B. Sudderth

ECCVW 2018 A Simple and Effective Fusion Approach for Multi-Frame Optical Flow Estimation Zhile Ren, Orazio Gallo, Deqing Sun, Ming-Hsuan Yang, Erik B. Sudderth, Jan Kautz

AISTATS 2018 Semi-Supervised Prediction-Constrained Topic Models Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez

ICML 2017 From Patches to Images: A Nonparametric Generative Model Geng Ji, Michael C. Hughes, Erik B. Sudderth

MLOSS 2017 Refinery: An Open Source Topic Modeling Web Platform Daeil Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth

CVPR 2016 Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients Zhile Ren, Erik B. Sudderth

CVPR 2015 Layered RGBD Scene Flow Estimation Deqing Sun, Erik B. Sudderth, Hanspeter Pfister

AISTATS 2015 Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process Michael C. Hughes, Dae Il Kim, Erik B. Sudderth

UAI 2014 Nonparametric Clustering with Distance Dependent Hierarchies Soumya Ghosh, Michalis Raptis, Leonid Sigal, Erik B. Sudderth

CVPR 2013 A Fully-Connected Layered Model of Foreground and Background Flow Deqing Sun, Jonas Wulff, Erik B. Sudderth, Hanspeter Pfister, Michael J. Black

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

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

CVPR 2012 Layered Segmentation and Optical Flow Estimation over Time Deqing Sun, Erik B. Sudderth, Michael J. Black

NeurIPS 2012 Minimization of Continuous Bethe Approximations: A Positive Variation Jason Pacheco, Erik B. Sudderth

CVPRW 2012 Nonparametric Discovery of Activity Patterns from Video Collections Michael C. Hughes, Erik B. Sudderth

CVPR 2012 Nonparametric Learning for Layered Segmentation of Natural Images Soumya Ghosh, Erik B. Sudderth

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

NeurIPS 2012 Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes Michael Bryant, Erik B. Sudderth

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

NeurIPS 2011 Spatial Distance Dependent Chinese Restaurant Processes for Image Segmentation Soumya Ghosh, Andrei B. Ungureanu, Erik B. Sudderth, David M. Blei

NeurIPS 2011 The Doubly Correlated Nonparametric Topic Model Dae I. Kim, Erik B. Sudderth

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

NeurIPS 2010 Global Seismic Monitoring as Probabilistic Inference Nimar Arora, Stuart Russell, Paul Kidwell, Erik B. Sudderth

NeurIPS 2010 Layered Image Motion with Explicit Occlusions, Temporal Consistency, and Depth Ordering Deqing Sun, Erik B. Sudderth, Michael J. Black

NeurIPS 2009 Sharing Features Among Dynamical Systems with Beta Processes Emily B. Fox, Michael I. Jordan, Erik B. Sudderth, Alan S. Willsky

ICML 2008 An HDP-HMM for Systems with State Persistence Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky

NeurIPS 2008 Nonparametric Bayesian Learning of Switching Linear Dynamical Systems Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky

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

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

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

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

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

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

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

CVPR 2004 Visual Hand Tracking Using Nonparametric Belief Propagation Erik B. Sudderth, Michael I. Mandel, William T. Freeman, Alan S. Willsky

CVPRW 2004 Visual Hand Tracking Using Nonparametric Belief Propagation Erik B. Sudderth, Michael I. Mandel, William T. Freeman, Alan S. Willsky

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

CVPR 2003 Nonparametric Belief Propagation Erik B. Sudderth, Alexander T. Ihler, William T. Freeman, Alan S. Willsky

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