Projects:
Theses:
- Graphical Models for Visual Object Recognition and Tracking.
- Doctoral Thesis, Massachusetts Institute of Technology, May 2006.
- Embedded Trees: Estimation of
Gaussian Processes on Graph with Cycles.
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Masters Thesis, Massachusetts Institute of Technology, Feb. 2002.
Papers:
- An HDP-HMM for Systems with State Persistence.
- E. Fox, E. Sudderth, M. Jordan, and A. Willsky.
To appear at the International Conference on Machine Learning, July 2008.
- Describing Visual Scenes Using Transformed Objects and Parts.
- E. Sudderth, A. Torralba, W. Freeman, and A. Willsky.
International Journal of Computer Vision 77, May 2008.
- Signal and Image Processing with Belief Propagation.
- E. Sudderth and W. Freeman.
DSP Application Column, IEEE Signal Processing Magazine, Mar. 2008.
- Loop Series and Bethe Variational Bounds in Attractive Graphical Models.
- E. Sudderth, M. Wainwright, and A. Willsky.
Neural Information Processing Systems, Dec. 2007.
- Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes.
- J. Kivinen, E. Sudderth, and M. Jordan.
IEEE International Conference on Computer Vision, Oct. 2007.
- Image Denoising with Nonparametric Hidden Markov Trees.
- J. Kivinen, E. Sudderth, and M. Jordan.
IEEE International Conference on Image Processing, Sep. 2007.
- Hierarchical Dirichlet Processes for Tracking Maneuvering Targets.
- E. Fox, E. Sudderth, and A. Willsky.
To appear at the International Conference on Information Fusion, July 2007.
- Depth from Familiar Objects: A Hierarchical Model for 3D Scenes.
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E. Sudderth, A. Torralba, W. Freeman, and A. Willsky.
IEEE Conference on Computer Vision & Pattern Recognition, June 2006.
- Describing Visual Scenes using Transformed Dirichlet Processes.
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E. Sudderth, A. Torralba, W. Freeman, and A. Willsky.
Neural Information Processing Systems, Dec. 2005.
- Learning Hierarchical Models of Scenes, Objects, and Parts.
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E. Sudderth, A. Torralba, W. Freeman, and A. Willsky.
International Conference on Computer Vision, Oct. 2005.
- Distributed Occlusion Reasoning for
Tracking with Nonparametric Belief Propagation.
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E. Sudderth, M. Mandel, W. Freeman, and A. Willsky.
Neural Information Processing Systems, Dec. 2004.
- Embedded Trees: Estimation of Gaussian
Processes on Graphs with Cycles.
-
E. Sudderth, M. Wainwright, and A. Willsky.
IEEE Transactions on Signal Processing 52(11), Nov. 2004.
An earlier version appeared as MIT LIDS Technical
Report 2562, Apr. 2003.
- Visual Hand Tracking Using Nonparametric
Belief Propagation.
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E. Sudderth, M. Mandel, W. Freeman, and A. Willsky.
Workshop on Generative Model Based Vision, CVPR, June 2004.
- Efficient Multiscale Sampling from Products
of Gaussian Mixtures.
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A. Ihler, E. Sudderth, W. Freeman, and A. Willsky.
Neural Information Processing Systems, Dec. 2003.
- Nonparametric Belief Propagation.
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E. Sudderth, A. Ihler, W. Freeman, and A. Willsky.
IEEE Conference on Computer Vision & Pattern Recognition, June 2003.
An earlier version appeared as MIT LIDS Technical
Report 2551, Oct. 2002.
- Projection Algebra Analysis of Error-Correcting
Codes.
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J. Yedidia, E. Sudderth, and J-P. Bouchaud.
Allerton Conference on Communication, Control, and Computing, Oct. 2001.
- Tree-Based Modeling and Estimation of
Gaussian Processes on Graphs with Cycles.
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M. Wainwright, E. Sudderth, and A. Willsky.
Neural Information Processing Systems, Dec. 2000.
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