Chris Williams
Chris Williams
Professor of Machine Learning, University of Edinburgh
Verified email at inf.ed.ac.uk
Title
Cited by
Cited by
Year
Gaussian processes for machine learning
CE Rasmussen, CKI Williams
MIT Press, 2006
187382006
Gaussian process for machine learning
CE Rasmussen, CKI Williams
MIT press, 2006
177072006
The PASCAL Visual Object Classes (VOC) challenge
M Everingham, L Van Gool, CKI Williams, J Winn, A Zisserman
Int J Computer Vision 88 (2), 303-338, 2010
82292010
The PASCAL visual object classes challenge 2007 (VOC2007) results
M Everingham, L Van Gool, CKI Williams, J Winn, A Zisserman
23932007
The pascal visual object classes challenge: A retrospective
M Everingham, SMA Eslami, L Van Gool, CKI Williams, J Winn, ...
International journal of computer vision 111 (1), 98-136, 2015
23592015
Using the Nyström method to speed up kernel machines
CKI Williams, M Seeger
Advances in neural information processing systems, 682-688, 2001
20462001
GTM: The generative topographic mapping
CM Bishop, M Svensén, CKI Williams
Neural computation 10 (1), 215-234, 1998
16481998
Gaussian processes for regression
CKI Williams, CE Rasmussen
Advances in neural information processing systems, 514-520, 1996
11091996
Multi-task Gaussian process prediction
EV Bonilla, KM Chai, C Williams
Advances in neural information processing systems, 153-160, 2008
7722008
Bayesian classification with Gaussian processes
CKI Williams, D Barber
IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (12), 1342 …, 1998
7691998
Prediction with Gaussian processes: From linear regression to linear prediction and beyond
CKI Williams
Learning in graphical models, 599-621, 1998
6851998
Fast forward selection to speed up sparse Gaussian process regression
M Seeger, C Williams, N Lawrence
Artificial Intelligence and Statistics 9, 2003
4302003
Using machine learning to focus iterative optimization
F Agakov, E Bonilla, J Cavazos, B Franke, G Fursin, MFP O'Boyle, ...
International Symposium on Code Generation and Optimization (CGO'06), 11 pp.-305, 2006
4292006
Regression with input-dependent noise: A Gaussian process treatment
PW Goldberg, CKI Williams, CM Bishop
Advances in neural information processing systems, 493-499, 1998
2871998
The 2005 pascal visual object classes challenge
M Everingham, A Zisserman, CKI Williams, L Van Gool, M Allan, ...
Machine Learning Challenges Workshop, 117-176, 2005
2842005
Dataset issues in object recognition
J Ponce, TL Berg, M Everingham, DA Forsyth, M Hebert, S Lazebnik, ...
Toward category-level object recognition, 29-48, 2006
2442006
Developments of the generative topographic mapping
CM Bishop, M Svensén, CKI Williams
Neurocomputing 21 (1-3), 203-224, 1998
2251998
Using generative models for handwritten digit recognition
M Revow, CKI Williams, GE Hinton
IEEE transactions on pattern analysis and machine intelligence 18 (6), 592-606, 1996
2191996
GTM: A principled alternative to the self-organizing map
CM Bishop, M Svensén, CKI Williams
Advances in neural information processing systems, 354-360, 1997
2181997
The effect of the input density distribution on kernel-based classifiers
C Williams, M Seeger
Proceedings of the 17th international conference on machine learning, 2000
2002000
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