Chris Williams

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Andrew ZissermanUniversity of OxfordEmail confirmado em robots.ox.ac.uk
Luc Van Goolprofessor computer vision INSAIT Sofia University, em. KU Leuven, em. ETHZ, Toyota Lab TRACEEmail confirmado em insait.ai
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeEmail confirmado em cam.ac.uk
S. M. Ali EslamiGoogle DeepMindEmail confirmado em google.com
Christopher M. BishopTechnical Fellow, Microsoft Research, Cambridge, U.K.Email confirmado em microsoft.com
Edwin V. BonillaPrincipal Research Scientist, CSIRO's Data61Email confirmado em data61.csiro.au
Geoffrey HintonEmeritus Prof. Computer Science, University of TorontoEmail confirmado em cs.toronto.edu
Matthias SeegerPrincipal Applied Scientist, Amazon, BerlinEmail confirmado em amazon.de
John Shawe-TaylorUCLEmail confirmado em cs.ucl.ac.uk
John QuinnGoogle Research, Sunbird AI, Makerere UniversityEmail confirmado em cit.ac.ug
Richard ZemelProfessor of Computer Science, University of TorontoEmail confirmado em cs.toronto.edu
David BarberDepartment of Computer Science, University College LondonEmail confirmado em ucl.ac.uk
Michael MozerGoogle DeepMind; University of ColoradoEmail confirmado em colorado.edu
Neil D. LawrenceUniversity of CambridgeEmail confirmado em cl.cam.ac.uk
Marc ToussaintProfessor of Computer Science, TU Berlin, GermanyEmail confirmado em tu-berlin.de
Miguel Á. Carreira-PerpiñánProfessor of Computer Science, University of California, MercedEmail confirmado em ucmerced.edu
Ian T. NabneyProfessor and Associate Dean, Faculty of Science and Engineering, University of BristolEmail confirmado em bristol.ac.uk
Joaquin Quiñonero CandelaOpenAIEmail confirmado em openai.com
Volker TrespLudwig-Maximilians-Universität München (LMU Munich)Email confirmado em dbs.ifi.lmu.de
Sethu VijayakumarProfessor of Robotics, University of Edinburgh; Programme Director at The Alan Turing Institute, UKEmail confirmado em ed.ac.uk

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Chris Williams

Professor of Machine Learning, University of Edinburgh

Email confirmado em inf.ed.ac.uk

Machine learning pattern recognition computer vision


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
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	24018	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	11649	2015
Gaussian Processes for Machine Learning CE Rasmussen, CKI Williams MIT Press, 2006	5481*	2006
Using the Nyström method to speed up kernel machines C Williams, M Seeger Advances in neural information processing systems 13, 2000	3129	2000
Gaussian processes for regression C Williams, C Rasmussen Advances in neural information processing systems 8, 1995	1978	1995
GTM: The generative topographic mapping CM Bishop, M Svensén, CKI Williams Neural computation 10 (1), 215-234, 1998	1934	1998
Multi-task Gaussian process prediction EV Bonilla, K Chai, C Williams Advances in neural information processing systems 20, 2007	1519	2007
Bayesian classification with Gaussian processes CKI Williams, D Barber IEEE Transactions on pattern analysis and machine intelligence 20 (12), 1342 …, 1998	1066	1998
Prediction with Gaussian processes: From linear regression to linear prediction and beyond CKI Williams Learning in graphical models, 599-621, 1998	995	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	671	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	534	2006
A framework for the quantitative evaluation of disentangled representations C Eastwood, CKI Williams 6th International Conference on Learning Representations, 2018	502	2018
Computing with infinite networks C Williams Advances in neural information processing systems 9, 1996	475	1996
Regression with input-dependent noise: A Gaussian process treatment P Goldberg, C Williams, C Bishop Advances in neural information processing systems 10, 1997	466	1997
On a connection between kernel PCA and metric multidimensional scaling C Williams Advances in neural information processing systems 13, 2000	351	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	335	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	321	2006
Harmonising chorales by probabilistic inference M Allan, C Williams Advances in neural information processing systems 17, 2004	278	2004
Developments of the generative topographic mapping CM Bishop, M Svensén, CKI Williams Neurocomputing 21 (1-3), 203-224, 1998	276	1998
Using generative models for handwritten digit recognition M Revow, CKI Williams, GE Hinton IEEE transactions on pattern analysis and machine intelligence 18 (6), 592-606, 1996	272	1996

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