• Publications
  • Influence
Expectation Propagation for approximate Bayesian inference
This paper presents a new deterministic approximation technique in Bayesian networks. This method, "Expectation Propagation," unifies two previous techniques: assumed-density filtering, an extensionExpand
  • 1,443
  • 141
A family of algorithms for approximate Bayesian inference
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 perform Bayesian inferenceExpand
  • 883
  • 124
Object categorization by learned universal visual dictionary
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 automaticallyExpand
  • 933
  • 101
Bayesian color constancy revisited
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,Expand
  • 299
  • 77
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
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 modelExpand
  • 530
  • 68
TrueSkillTM: A Bayesian Skill Rating System
We present a new Bayesian skill rating system which can be viewed as a generalisation of the Elo system used in Chess. The new system tracks the uncertainty about player skills, explicitly modelsExpand
  • 514
  • 67
The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments
This paper presents the theory, design principles, implementation and performance results of PicHunter, a prototype content-based image retrieval (CBIR) system. In addition, this document presentsExpand
  • 774
  • 50
Automatic Choice of Dimensionality for PCA
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 modelExpand
  • 468
  • 41
Novelty and redundancy detection in adaptive filtering
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 andExpand
  • 477
  • 36
SoftRank: optimizing non-smooth rank metrics
We address the problem of learning large complex ranking functions. Most IR applications use evaluation metrics that depend only upon the ranks of documents. However, most ranking functions generateExpand
  • 267
  • 30