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- Tom Minka
- 2001

One of the major obstacles to using Bayesian methods for pattern recognition has been its computational expense This thesis presents an approximation technique that can per form Bayesian inference faster and more accurately than previously possible This method Expectation Propagation uni es and generalizes two previous techniques assumed density ltering an… (More)

- Tom Minka
- UAI
- 2001

This paper presents a new deterministic approximation technique in Bayesian networks. This method, “Expectation Propagation,” unifies two previous techniques: assumed-density filtering, an extension of the Kalman filter, and loopy belief propagation, an extension of belief propagation in Bayesian networks. Loopy belief propagation, because it propagates… (More)

- John M. Winn, Antonio Criminisi, Tom Minka
- Tenth IEEE International Conference on Computer…
- 2005

This paper presents a new algorithm for the automatic recognition of object classes from images (categorization). Compact and yet discriminative appearance-based object class models are automatically learned from a set of training images. The method is simple and extremely fast, making it suitable for many applications such as semantic image retrieval, Web… (More)

- Carsten Rother, Tom Minka, Andrew Blake, Vladimir Kolmogorov
- 2006 IEEE Computer Society Conference on Computer…
- 2006

We introduce the term cosegmentation which denotes the task of segmenting simultaneously the common parts of an image pair. A generative model for cosegmentation is presented. Inference in the model leads to minimizing an energy with an MRF term encoding spatial coherency and a global constraint which attempts to match the appearance histograms of the… (More)

- Ralf Herbrich, Tom Minka, Thore Graepel
- NIPS
- 2006

- Tom Minka
- NIPS
- 2000

A central issue in principal component analysis (PCA) is choosing the number of principal components to be retained. By interpreting PCA as density estimation, we show how to use Bayesian model selection to estimate the true dimensionality of the data. The resulting estimate is simple to compute yet guaranteed to pick the correct dimensionality, given… (More)

- Peter V. Gehler, Carsten Rother, Andrew Blake, Tom Minka, Toby Sharp
- 2008 IEEE Conference on Computer Vision and…
- 2008

Computational color constancy is the task of estimating the true reflectances of visible surfaces in an image. In this paper we follow a line of research that assumes uniform illumination of a scene, and that the principal step in estimating reflectances is the estimation of the scene illuminant. We review recent approaches to illuminant estimation, firstly… (More)

- Yi Zhang, James P. Callan, Tom Minka
- SIGIR
- 2002

This paper addresses the problem of extending an adaptive information filtering system to make decisions about the novelty and redundancy of relevant documents. It argues that relevance and redundance should each be modelled explicitly and separately. A set of five redundancy measures are proposed and evaluated in experiments with and without redundancy… (More)

- Julia A. Lasserre, Christopher M. Bishop, Tom Minka
- 2006 IEEE Computer Society Conference on Computer…
- 2006

When labelled training data is plentiful, discriminative techniques are widely used since they give excellent generalization performance. However, for large-scale applications such as object recognition, hand labelling of data is expensive, and there is much interest in semi-supervised techniques based on generative models in which the majority of the… (More)

- Fan Guo, Chao Liu, +4 authors Christos Faloutsos
- WWW
- 2009

Given a terabyte click log, can we build an efficient and effective click model? It is commonly believed that web search click logs are a gold mine for search business, because they reflect users' preference over web documents presented by the search engine. Click models provide a principled approach to inferring user-perceived relevance of web documents,… (More)