Chris Williams

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Luc Van Goolprofessor computer vision ETH Zurich,KU Leuven,INSAIT,Head Toyota Lab TRACE @KUL&ETHVerified email at ethz.ch
Andrew ZissermanUniversity of OxfordVerified email at robots.ox.ac.uk
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. Comp Sci, U.Toronto & Engineering Fellow, GoogleVerified 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 Research, Brain Team; 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	29544*	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	21551	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	5749	2015
Using the Nyström method to speed up kernel machines C Williams, M Seeger Advances in neural information processing systems 13, 2000	2821	2000
GTM: The generative topographic mapping CM Bishop, M Svensén, CKI Williams Neural computation 10 (1), 215-234, 1998	1844	1998
Gaussian processes for regression C Williams, C Rasmussen Advances in neural information processing systems 8, 1995	1585	1995
Multi-task Gaussian process prediction EV Bonilla, K Chai, C Williams Advances in neural information processing systems 20, 2007	1250	2007
Bayesian classification with Gaussian processes CKI Williams, D Barber IEEE Transactions on pattern analysis and machine intelligence 20 (12), 1342 …, 1998	975	1998
Prediction with Gaussian processes: From linear regression to linear prediction and beyond CKI Williams Learning in graphical models, 599-621, 1998	912	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	596	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	502	2006
Regression with input-dependent noise: A Gaussian process treatment P Goldberg, C Williams, C Bishop Advances in neural information processing systems 10, 1997	398	1997
The pascal visual object classes challenge 2007 (voc 2007) results (2007) M Everingham, L Van Gool, CKI Williams, J Winn, A Zisserman	372	2008
Computing with infinite networks C Williams Advances in neural information processing systems 9, 1996	358	1996
A framework for the quantitative evaluation of disentangled representations C Eastwood, CKI Williams International Conference on Learning Representations, 2018	353	2018
The 2005 pascal visual object classes challenge M Everingham, A Zisserman, CKI Williams, L Van Gool, M Allan, ... Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual …, 2006	350	2006
On a connection between kernel PCA and metric multidimensional scaling C Williams Advances in neural information processing systems 13, 2000	330	2000
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	305	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, 296-327, 2011	304	2011
The PASCAL visual object classes challenge 2009 (VOC2009) results M Everingham http://www. pascal-network. org/challenges/VOC/voc2009/workshop/index. html, 2007	274*	2007

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