Chris Williams
Chris Williams
Professor of Machine Learning, University of Edinburgh
Verified email at
Cited by
Cited by
Gaussian processes for machine learning
CE Rasmussen, CKI Williams
MIT Press, 2006
Gaussian process for machine learning
CE Rasmussen, CKI Williams
MIT press, 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
Using the Nyström method to speed up kernel machines
C Williams, M Seeger
Advances in neural information processing systems 13, 2000
GTM: The generative topographic mapping
CM Bishop, M Svensén, CKI Williams
Neural computation 10 (1), 215-234, 1998
Gaussian processes for regression
C Williams, C Rasmussen
Advances in neural information processing systems 8, 1995
Multi-task Gaussian process prediction
EV Bonilla, K Chai, C Williams
Advances in neural information processing systems 20, 2007
Bayesian classification with Gaussian processes
CKI Williams, D Barber
IEEE Transactions on pattern analysis and machine intelligence 20 (12), 1342 …, 1998
Prediction with Gaussian processes: From linear regression to linear prediction and beyond
CKI Williams
Learning in graphical models, 599-621, 1998
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
Proceedings of the 32nd International Conference on Neural Information Processing Systems
S Bengio, HM Wallach, H Larochelle, K Grauman, N Cesa-Bianchi
Curran Associates Inc., 2018
Gaussian processes for machine learning, vol. 2
CK Williams, CE Rasmussen
MA: MIT press Cambridge, 2006
Computing with infinite networks
C Williams
Advances in neural information processing systems 9, 1996
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
Regression with input-dependent noise: A Gaussian process treatment
P Goldberg, C Williams, C Bishop
Advances in neural information processing systems 10, 1997
The 2005 pascal visual object classes challenge
M Everingham, A Zisserman, CKI Williams, LV Gool, M Allan, CM Bishop, ...
Machine Learning Challenges Workshop, 117-176, 2005
Resin infusion under flexible tooling (RIFT): a review
C Williams, J Summerscales, S Grove
Composites Part A: Applied Science and Manufacturing 27 (7), 517-524, 1996
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
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 (3), 296-327, 2011
A framework for the quantitative evaluation of disentangled representations
C Eastwood, CKI Williams
International Conference on Learning Representations, 2018
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