Chris Williams

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Luc Van Goolprofessor computer vision ETH Zurich & KU Leuven, 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
S. M. Ali EslamiGoogle DeepMindVerified email at google.com
Christopher M. BishopDistinguished Scientist, Microsoft Research, Cambridge, U.K.Verified email at microsoft.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 CandelaSenior Fellow, Harvard Belfer CenterVerified email at hks.harvard.edu
Volker TrespLudwig Maximilian University of Munich / München (LMU) and Siemens AGVerified 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	27910	2006
Gaussian process for machine learning CE Rasmussen, CKI Williams MIT press, 2006	26267	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	18751*	2010
Using the Nyström method to speed up kernel machines C Williams, M Seeger Advances in neural information processing systems 13, 2000	2587	2000
GTM: The generative topographic mapping CM Bishop, M Svensén, CKI Williams Neural computation 10 (1), 215-234, 1998	1783	1998
Gaussian processes for regression C Williams, C Rasmussen Advances in neural information processing systems 8, 1995	1428	1995
Multi-task Gaussian process prediction EV Bonilla, K Chai, C Williams Advances in neural information processing systems 20, 2007	1099	2007
Bayesian classification with Gaussian processes CKI Williams, D Barber IEEE Transactions on pattern analysis and machine intelligence 20 (12), 1342 …, 1998	915	1998
Prediction with Gaussian processes: From linear regression to linear prediction and beyond CKI Williams Learning in graphical models, 599-621, 1998	850	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	543	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	496	2018
Computing with infinite networks C Williams Advances in neural information processing systems 9, 1996	483	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	481	2006
Gaussian processes for machine learning, vol. 2 CK Williams, CE Rasmussen MA: MIT press Cambridge, 2006	450	2006
Regression with input-dependent noise: A Gaussian process treatment P Goldberg, C Williams, C Bishop Advances in neural information processing systems 10, 1997	362	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	324	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	291	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	288	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	280	2011
A framework for the quantitative evaluation of disentangled representations C Eastwood, CKI Williams International Conference on Learning Representations, 2018	249	2018

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