Machine Learning

Best-Buddies Similarity for Robust Template Matching

Tali Dekel, Shaul Oron, Michael Rubinstein, Shai Avidan, William T. Freeman
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015)
Paper (pdf)

Group Norm for Learning Structured SVMs with Unstructured Latent Variables

Daowen Chen, Dhruv Batra, William T. Freeman
2013 IEEE International Conference on Computer Vision (ICCV)
Paper (pdf)

Exploiting compositionality to explore a large space of model structures

Roger B. Grosse, Ruslan Salakhutdinov, William T. Freeman, and Joshua B. Tenenbaum
Conf. on Uncertainty in Artificial Intelligence (UAI), August 2012

Best Student Paper Prize

Paper (pdf)

Nonparametric Belief Propagation

Erik B. Sudderth, Alexander T. Ihler, Michael Isard, William T. Freeman, and Alan S. Willsky
Communications of the ACM, October, 2010
Paper (pdf)

Signal and Image Processing with Belief Propagation

E. Sudderth and W. T. Freeman
DSP Application Column, IEEE Signal Processing Magazine, Mar. 2008
Paper (pdf)

What makes a good model of natural images?

Y. Weiss and W. T. Freeman
IEEE Computer Vision and Pattern Recognition (CVPR) 2007
Paper (pdf)

Sharing visual features for multiclass and multiview object detection

A. Torralba, K. P. Murphy, and W. T. Freeman
IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 29, no. 5, pp. 854-869, May, 2007
Paper (pdf)

Shared features for multiclass object detection

A. Torralba, K. Murphy, W. T. Freeman
Towards Category-Level Object Recognition. Springer Lecture Notes in Computer Science (invited submission). 2005
Paper (pdf)

Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms

J. S. Yedidia, W. T. Freeman, and Y. Weiss
IEEE Transactions on Information Theory, ISSN; 0018-9448, Vol. 51, Issue 7, pp. 2282-2312, July 2005
Paper (pdf)

Efficient multiscale sampling from products of Gaussian mixtures

A. T. Ihler, E. B. Sudderth, W. T. Freeman, and A. S. Willsky
Advances in Neural Information Processing Systems 16 (NIPS), Vancouver, BC, MIT Press, 2004
Project website

Sharing visual features for multiclass and multiview object detection

A. Torralba, K. P. Murphy, and W. T. Freeman
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) Washington, DC, 2004; MIT CSAIL technical report
Paper (pdf)

Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters

M. F. Tappen and W. T. Freeman
IEEE Intl. Conference on Computer Vision (ICCV), Nice, France, October, 2003
Paper (pdf)

Nonparametric Belief Propagation and Facial Appearance Estimation

E. B. Sudderth, A. T. Ihler, W. T. Freeman and A. S. Willsky
IEEE Computer Vision and Pattern Recognition (CVPR), Madison, WI, June, 2003
Project website

Shape Recipes: Scene Representations that Refer to the Image

William T. Freeman, Antonio Torralba
Neural Information Processing Systems (NIPS) 2002
Paper (pdf)

Generalized Belief Propagation

J. Yedidia, W. T. Freeman, and Y. Weiss
Neural Information Processing Systems 13, edited by T. K. Leen, T. G. dietterich, and V. Tresp, pp. 689-695, 2001
Paper (pdf)

Learning Joint Statistical Models for Audio-Visual Fusion and Segregation

J. W. Fisher, T. Darrell, W. T. Freeman and P. Viola
Advances in Neural Information Processing Systems 13, edited by T. K. Leen, T. G. dietterich, and V. Tresp, pp. 772-778, 2001

On the optimality of solutions of the max-product belief propagation algorithm in arbitrary graphs

Y. Weiss and W. T. Freeman
IEEE Trans. Information Theory, Special Issue on Codes on Graphs and Iterative Algorithms, 47(2), pp. 723-735, 2001
Paper (pdf)

Understanding belief propagation and its generalizations

J. Yedidia, W. T. Freeman and Y. Weiss
International Joint Conference on Artificial Intelligence (IJCAI 2001), Distinguished Papers Track
Paper (pdf)

Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology

Y. Weiss and W. T. Freeman
Advances in Neural Information Processing Systems 12, edited by S. A. Solla, T. K. Leen, and K-R Muller, 2000
Paper (pdf)

Learning Low-Level Vision

W. T. Freeman, E. C. Pasztor, O. T. Carmichael
International Journal of Computer Vision, 40(1), pp. 25-47, 2000
Paper (pdf)

A factorization approach to grouping

P. Perona and W. T. Freeman
Proceedings, European Conference on Computer Vision, 1998
Paper (pdf)

Bayesian decision theory, the maximum local mass estimate, and color constancy

W. T. Freeman and D. H. Brainard
Fifth International Conference on Computer Vision, IEEE Computer Society, Cambridge, MA, U.S.A, June, 1995, pp. 210 - 217
Paper (pdf)