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Chris Williams
Chris Williams
Professor of Machine Learning, University of Edinburgh
Dirección de correo verificada de inf.ed.ac.uk
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Citado por
Citado por
Año
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
225782010
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
69122015
Gaussian processes for machine learning
CE Rasmussen, CKI Williams
MIT Press, 2006
4622*2006
The PASCAL visual object classes challenge 2008 (VOC2008) results
M Everingham
http://www. pascal-network. org/challenges/VOC/voc2008/year= workshop/index …, 2008
36502008
Using the Nyström method to speed up kernel machines
C Williams, M Seeger
Advances in neural information processing systems 13, 2000
30372000
GTM: The generative topographic mapping
CM Bishop, M Svensén, CKI Williams
Neural computation 10 (1), 215-234, 1998
19011998
Gaussian processes for regression
C Williams, C Rasmussen
Advances in neural information processing systems 8, 1995
18381995
Multi-task Gaussian process prediction
EV Bonilla, K Chai, C Williams
Advances in neural information processing systems 20, 2007
14312007
Bayesian classification with Gaussian processes
CKI Williams, D Barber
IEEE Transactions on pattern analysis and machine intelligence 20 (12), 1342 …, 1998
10381998
Prediction with Gaussian processes: From linear regression to linear prediction and beyond
CKI Williams
Learning in graphical models, 599-621, 1998
9711998
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
6562003
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
5252006
A framework for the quantitative evaluation of disentangled representations
C Eastwood, CKI Williams
International conference on learning representations, 2018
4642018
Regression with input-dependent noise: A Gaussian process treatment
P Goldberg, C Williams, C Bishop
Advances in neural information processing systems 10, 1997
4481997
Computing with infinite networks
C Williams
Advances in neural information processing systems 9, 1996
4351996
On a connection between kernel PCA and metric multidimensional scaling
C Williams
Advances in neural information processing systems 13, 2000
3472000
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
3252011
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
3152006
Harmonising chorales by probabilistic inference
M Allan, C Williams
Advances in neural information processing systems 17, 2004
2702004
Developments of the generative topographic mapping
CM Bishop, M Svensén, CKI Williams
Neurocomputing 21 (1-3), 203-224, 1998
2701998
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Artículos 1–20