Chris Williams

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Andrew ZissermanUniversity of OxfordVerified email at robots.ox.ac.uk
Luc Van Goolprofessor computer vision ETH Zurich,KU Leuven,INSAIT,Head Toyota Lab TRACE @KUL&ETHVerified email at ethz.ch
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Christopher M. BishopTechnical Fellow, Microsoft Research, Cambridge, U.K.Verified email at microsoft.com
S. M. Ali EslamiGoogle DeepMindVerified email at google.com
Edwin V. BonillaPrincipal Research Scientist, CSIRO's Data61Verified email at data61.csiro.au
Geoffrey HintonEmeritus Prof. Computer Science, University of TorontoVerified email at cs.toronto.edu
Matthias SeegerPrincipal Applied Scientist, Amazon, BerlinVerified email at amazon.de
John QuinnGoogle Research, Sunbird AI, Makerere UniversityVerified email at cit.ac.ug
Richard ZemelProfessor of Computer Science, University of TorontoVerified email at cs.toronto.edu
David BarberDepartment of Computer Science, University College LondonVerified email at ucl.ac.uk
John Shawe-TaylorUCLVerified email at cs.ucl.ac.uk
Michael MozerGoogle DeepMind; University of ColoradoVerified email at colorado.edu
Jyri J. KivinenLapland University of Applied SciencesVerified email at lapinamk.fi
Neil D. LawrenceUniversity of CambridgeVerified email at cl.cam.ac.uk
Marc ToussaintProfessor of Computer Science, TU Berlin, GermanyVerified email at tu-berlin.de
Miguel Á. Carreira-PerpiñánProfessor of Computer Science, University of California, MercedVerified email at ucmerced.edu
Ian T. NabneyProfessor and Head of SCEEM, University of BristolVerified email at bristol.ac.uk
Joaquin Quiñonero CandelaAI Technical Fellow at LinkedInVerified email at linkedin.com
Volker TrespLudwig Maximilian University of Munich / München (LMU)Verified email at dbs.ifi.lmu.de

Chris Williams

Professor of Machine Learning, University of Edinburgh

Verified email at inf.ed.ac.uk

Machine learning pattern recognition computer vision


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Gaussian processes for machine learning CE Rasmussen, CKI Williams MIT Press, 2006	32265*	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	20887	2010
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	6304	2015
The PASCAL visual object classes challenge 2008 (VOC2008) results M Everingham http://www. pascal-network. org/challenges/VOC/voc2008/year= workshop/index …, 2008	3444	2008
Using the Nyström method to speed up kernel machines C Williams, M Seeger Advances in neural information processing systems 13, 2000	2909	2000
GTM: The generative topographic mapping CM Bishop, M Svensén, CKI Williams Neural computation 10 (1), 215-234, 1998	1864	1998
Gaussian processes for regression C Williams, C Rasmussen Advances in neural information processing systems 8, 1995	1687	1995
Multi-task Gaussian process prediction EV Bonilla, K Chai, C Williams Advances in neural information processing systems 20, 2007	1346	2007
Bayesian classification with Gaussian processes CKI Williams, D Barber IEEE Transactions on pattern analysis and machine intelligence 20 (12), 1342 …, 1998	1000	1998
Prediction with Gaussian processes: From linear regression to linear prediction and beyond CKI Williams Learning in graphical models, 599-621, 1998	956	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	628	2003
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	516	2006
Regression with input-dependent noise: A Gaussian process treatment P Goldberg, C Williams, C Bishop Advances in neural information processing systems 10, 1997	425	1997
A framework for the quantitative evaluation of disentangled representations C Eastwood, CKI Williams International conference on learning representations, 2018	416	2018
Computing with infinite networks C Williams Advances in neural information processing systems 9, 1996	392	1996
The pascal visual object classes challenge 2007 (voc 2007) results (2007) M Everingham, L Van Gool, CKI Williams, J Winn, A Zisserman	386	2008
On a connection between kernel PCA and metric multidimensional scaling C Williams Advances in neural information processing systems 13, 2000	337	2000
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	314	2011
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	309	2006
Harmonising chorales by probabilistic inference M Allan, C Williams Advances in neural information processing systems 17, 2004	266	2004

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