Chris Williams

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Andrew ZissermanUniversity of OxfordPatvirtintas el. paštas robots.ox.ac.uk
Luc Van Goolprofessor computer vision INSAIT Sofia University, em. KU Leuven, em. ETHZ, Toyota Lab TRACEPatvirtintas el. paštas insait.ai
S. M. Ali EslamiGoogle DeepMindPatvirtintas el. paštas google.com
Christopher M. BishopTechnical Fellow, Microsoft Research, Cambridge, U.K.Patvirtintas el. paštas microsoft.com
Edwin V. BonillaPrincipal Research Scientist, CSIRO's Data61Patvirtintas el. paštas data61.csiro.au
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgePatvirtintas el. paštas cam.ac.uk
Geoffrey HintonEmeritus Prof. Computer Science, University of TorontoPatvirtintas el. paštas cs.toronto.edu
Matthias SeegerPrincipal Applied Scientist, Amazon, BerlinPatvirtintas el. paštas amazon.de
John Shawe-TaylorUCLPatvirtintas el. paštas cs.ucl.ac.uk
Richard ZemelProfessor of Computer Science, University of TorontoPatvirtintas el. paštas cs.toronto.edu
John QuinnGoogle Research, Sunbird AI, Makerere UniversityPatvirtintas el. paštas cit.ac.ug
David BarberDepartment of Computer Science, University College LondonPatvirtintas el. paštas ucl.ac.uk
Michael MozerGoogle DeepMind; University of ColoradoPatvirtintas el. paštas colorado.edu
Neil D. LawrenceUniversity of CambridgePatvirtintas el. paštas cl.cam.ac.uk
Marc ToussaintProfessor of Computer Science, TU Berlin, GermanyPatvirtintas el. paštas tu-berlin.de
Miguel Á. Carreira-PerpiñánProfessor of Computer Science, University of California, MercedPatvirtintas el. paštas ucmerced.edu
Ian T. NabneyProfessor and Head of SCEEM, University of BristolPatvirtintas el. paštas bristol.ac.uk
Joaquin Quiñonero CandelaOpenAIPatvirtintas el. paštas openai.com
Volker TrespLudwig-Maximilians-Universität München (LMU Munich)Patvirtintas el. paštas dbs.ifi.lmu.de
Sethu VijayakumarProfessor of Robotics, University of Edinburgh; Programme Director at The Alan Turing Institute, UKPatvirtintas el. paštas ed.ac.uk

Stebėti

Chris Williams

Professor of Machine Learning, University of Edinburgh

Patvirtintas el. paštas inf.ed.ac.uk

Machine learning pattern recognition computer vision


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	23491	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	11421	2015
Gaussian processes for machine learning CE Rasmussen, CKI Williams MIT Press, 2006	5254*	2006
Using the Nyström method to speed up kernel machines C Williams, M Seeger Advances in neural information processing systems 13, 2000	3100	2000
Gaussian processes for regression C Williams, C Rasmussen Advances in neural information processing systems 8, 1995	1928	1995
GTM: The generative topographic mapping CM Bishop, M Svensén, CKI Williams Neural computation 10 (1), 215-234, 1998	1919	1998
Multi-task Gaussian process prediction EV Bonilla, K Chai, C Williams Advances in neural information processing systems 20, 2007	1488	2007
Bayesian classification with Gaussian processes CKI Williams, D Barber IEEE Transactions on pattern analysis and machine intelligence 20 (12), 1342 …, 1998	1062	1998
Prediction with Gaussian processes: From linear regression to linear prediction and beyond CKI Williams Learning in graphical models, 599-621, 1998	984	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	665	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	532	2006
A framework for the quantitative evaluation of disentangled representations C Eastwood, CKI Williams 6th International Conference on Learning Representations, 2018	485	2018
Computing with infinite networks C Williams Advances in neural information processing systems 9, 1996	464	1996
Regression with input-dependent noise: A Gaussian process treatment P Goldberg, C Williams, C Bishop Advances in neural information processing systems 10, 1997	458	1997
On a connection between kernel PCA and metric multidimensional scaling C Williams Advances in neural information processing systems 13, 2000	348	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	333	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	320	2006
Harmonising chorales by probabilistic inference M Allan, C Williams Advances in neural information processing systems 17, 2004	276	2004
Developments of the generative topographic mapping CM Bishop, M Svensén, CKI Williams Neurocomputing 21 (1-3), 203-224, 1998	273	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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