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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
33397*2006
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
216502010
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, 98-136, 2015
65882015
The PASCAL visual object classes challenge 2008 (VOC2008) results
M Everingham
http://www. pascal-network. org/challenges/VOC/voc2008/year= workshop/index …, 2008
35282008
Using the Nyström method to speed up kernel machines
C Williams, M Seeger
Advances in neural information processing systems 13, 2000
29762000
GTM: The generative topographic mapping
CM Bishop, M Svensén, CKI Williams
Neural computation 10 (1), 215-234, 1998
18861998
Gaussian processes for regression
C Williams, C Rasmussen
Advances in neural information processing systems 8, 1995
17511995
Multi-task Gaussian process prediction
EV Bonilla, K Chai, C Williams
Advances in neural information processing systems 20, 2007
13942007
Bayesian classification with Gaussian processes
CKI Williams, D Barber
IEEE Transactions on pattern analysis and machine intelligence 20 (12), 1342 …, 1998
10121998
Prediction with Gaussian processes: From linear regression to linear prediction and beyond
CKI Williams
Learning in graphical models, 599-621, 1998
9681998
Fast forward selection to speed up sparse Gaussian process regression
MW Seeger, CKI Williams, ND Lawrence
International Workshop on Artificial Intelligence and Statistics, 254-261, 2003
6452003
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
5202006
A framework for the quantitative evaluation of disentangled representations
C Eastwood, CKI Williams
International conference on learning representations, 2018
4382018
Regression with input-dependent noise: A Gaussian process treatment
P Goldberg, C Williams, C Bishop
Advances in neural information processing systems 10, 1997
4361997
Computing with infinite networks
C Williams
Advances in neural information processing systems 9, 1996
4071996
The pascal visual object classes challenge 2007 (voc 2007) results (2007)
M Everingham, L Van Gool, CKI Williams, J Winn, A Zisserman
3922008
On a connection between kernel PCA and metric multidimensional scaling
C Williams
Advances in neural information processing systems 13, 2000
3412000
Milepost gcc: Machine learning enabled self-tuning compiler
G Fursin, Y Kashnikov, AW Memon, Z Chamski, O Temam, M Namolaru, ...
International journal of parallel programming 39, 296-327, 2011
3202011
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
3142006
The shape boltzmann machine: a strong model of object shape
SMA Eslami, N Heess, CKI Williams, J Winn
International journal of computer vision 107, 155-176, 2014
2712014
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