Related meetings

Fast multipole methods CSCAMM Program Spring 2004

Data structures for fast statistics ICML 2004 Tutorial

Papers

CMU Auton Lab papers on kd-trees for fast statistical computation.

Reconstruction and representation of 3D objects with radial basis functions J. C. Carr, R. K. Beatson, J. B. Cherrie, T. J. Mitchell, W. R. Fright, B. C. McCallum, and T. R. Evans. Computer Graphics (SIGGRAPH 2001 proceedings), pp. 67-76, August 2001.

A short course on fast multipole methods R. K. Beatson and L. Greengard. In Wavelets, Multilevel Methods and Elliptic PDEs (M. Ainsworth, J. Levesley, W. Light, and M. Marletta, eds.), pp. 1-37, Oxford University Press, 1997.

Mike Klaas, Dustin Lang and Nando de Freitas. Fast Maximum a Posteriori Inference in Monte Carlo State Spaces . AISTATS 2005. PDF

Maryam Mahdaviani, Nando de Freitas, Bob Fraser and Firas Hamze. Fast Computational Methods for Visually Guided Robots. ICRA 2005. PDF

Distinctive image features from scale-invariant keypoints David G. Lowe, International Journal of Computer Vision, 60, 2, pp. 91-110, 2004.

Fast Algorithms for Large-State-Space HMMs with Applications to Web Usage Analysis Pedro F. Felzenszwalb, Daniel P. Huttenlocher and Jon M. Kleinberg. NIPS, 2003.

Beat Tracking the Graphical Model Way. Dustin Lang and Nando de Freitas. NIPS 2004.

Nonparametric Belief Propagation E. B. Sudderth, A. T. Ihler, W. T. Freeman, and A. S. Willsky. In Proceedings, Computer Vision and Pattern Recognition (CVPR) 2003.

Efficient Multiscale Sampling from Products of Gaussian Mixtures A. T. Ihler, E. B. Sudderth, W. T. Freeman, and A. S. Willsky. In Proceedings, Neural Information Processing Systems (NIPS) 2003.

Nonparametric Belief Propagation for Self-Calibration in Sensor Networks A. T. Ihler, J. W. Fisher, R. L. Moses, and A. S. Willsky. In Proceedings, Information Processing in Sensor Networks (IPSN) 2004.

Visual Hand Tracking Using Nonparametric Belief Propagation E. Sudderth, M. Mandel, W. Freeman, and A. Willsky. Workshop on Generative Model Based Vision, CVPR, June 2004.

Nonparametric Hypothesis Tests for Statistical Dependency A. T. Ihler, J. W. Fisher, and A. S. Willsky. In IEEE Transactions on Signal Processing, pp. 2234-2249, August 2004.

Software

CMU Auton Lab software.

Efficient Belief Propagation for Early Vision. Pedro F. Felzenszwalb and Daniel P. Huttenlocher.

Kernel Density Estimation Class Alexander Ihler. An efficient (KD-tree based) Matlab/MEX implementation of multidimensional kernel density estimation. Supports Gaussian, Laplacian and Epanetchnikov (product) kernels.