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» Complexity of Inference in Graphical Models
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NIPS
2003
13 years 9 months ago
Pairwise Clustering and Graphical Models
Significant progress in clustering has been achieved by algorithms that are based on pairwise affinities between the datapoints. In particular, spectral clustering methods have ...
Noam Shental, Assaf Zomet, Tomer Hertz, Yair Weiss
ICMCS
1999
IEEE
164views Multimedia» more  ICMCS 1999»
13 years 12 months ago
A Semi-Automatic System to Infer Complex 3-D Shapes from Photographs
We present a semi-automatic image-based modeling system to make 3-D models from photographs. The human operator intervenes only for simple and straightforward tasks. The design of...
Qian Chen, Gérard G. Medioni
KR
2004
Springer
14 years 29 days ago
Inferential Complexity Control for Model-Based Abduction
We describe a technique for speeding up inference for model-based abduction tasks that trades off inference time and/or space for the fraction of queries correctly answered. We co...
Gregory M. Provan
CORR
2012
Springer
170views Education» more  CORR 2012»
12 years 3 months ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson
ICML
2006
IEEE
14 years 8 months ago
Combining discriminative features to infer complex trajectories
We propose a new model for the probabilistic estimation of continuous state variables from a sequence of observations, such as tracking the position of an object in video. This ma...
David A. Ross, Simon Osindero, Richard S. Zemel