Amos Storkey - Google Scholar Citations

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	All	Since 2015
Citations	14139	10271
h-index	35	29
i10-index	72	53

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Co-authors

Chris WilliamsProfessor of Machine Learning, University of EdinburghVerified email at inf.ed.ac.uk
Harrison EdwardsOpenAIVerified email at openai.com
Jonathan D. ClaydenLecturer in Neuroimaging and Biophysics, UCL GOS Institute of Child Health, University CollegeVerified email at ucl.ac.uk
Krzysztof J. GorgolewskiGoogle LLCVerified email at google.com
Cyril PernetSenior Academic Fellow, University of EdinburghVerified email at ed.ac.uk
Antreas AntoniouUniversity of EdinburghVerified email at sms.ed.ac.uk
Marc ToussaintProfessor of Computer Science, University of Stuttgart, GermanyVerified email at informatik.uni-stuttgart.de
Yoshua BengioProfessor, University of Montreal (Computer Sc. & Op. Res.), Mila, CIFAR, CRM, IVADO, REPARTI, GRSNCVerified email at mila.quebec
Susana Muñoz ManiegaResearch Fellow, University of EdinburghVerified email at ed.ac.uk
David P. ReichertGoogle DeepMindVerified email at google.com
Romain ValabregueCENIR ICMVerified email at upmc.fr
Peggy SerièsSenior Lecturer in Informatics, University EdinburghVerified email at inf.ed.ac.uk
S LawrieProfessor of Psychiatry, University of EdinburghVerified email at ed.ac.uk
Lawrence MurrayUber AIVerified email at uber.com
Andrew DaiGoogle BrainVerified email at google.com
Stefan HarmelingProfessor at Heinrich-Heine-Universität DüsseldorfVerified email at hhu.de
Zoubin GhahramaniProfessor, University of Cambridge, and Chief Scientist, UberVerified email at eng.cam.ac.uk
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
Alan BundyProfessor of Automated Reasoning, University of EdinburghVerified email at ed.ac.uk
Krzysztof J. GerasNew York UniversityVerified email at nyu.edu

Amos Storkey

Professor of Machine Learning and AI, School of Informatics, University of Edinburgh, UK

Verified email at ed.ac.uk

Deep Learning Machine Learning Bayesian Methods Prediction Markets diffusion MRI


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The pascal visual object classes (voc) challenge M Everingham, L Van Gool, CKI Williams, J Winn, A Zisserman International journal of computer vision 88 (2), 303-338, 2010	10045*	2010
The 2005 pascal visual object classes challenge M Everingham, A Zisserman, CKI Williams, L Van Gool, M Allan, ... Machine Learning Challenges Workshop, 117-176, 2005	279	2005
Data augmentation generative adversarial networks A Antoniou, A Storkey, H Edwards arXiv preprint arXiv:1711.04340, 2017	226	2017
Censoring Representations with an Adversary H Edwards, A Storkey Proceedings of ICLR and arXiv preprint arXiv:1511.05897, 2015	173	2015
Probabilistic inference for solving discrete and continuous state Markov Decision Processes M Toussaint, A Storkey Proceedings of the 23rd international conference on Machine learning, 945-952, 2006	173	2006
Towards a neural statistician H Edwards, A Storkey arXiv preprint arXiv:1606.02185, 2016	171	2016
Training deep convolutional neural networks to play go C Clark, A Storkey arXiv:1412.3409, 2014, and Proceedings of the 32nd International Conference …, 2014	165	2014
Large-scale study of curiosity-driven learning Y Burda, H Edwards, D Pathak, A Storkey, T Darrell, AA Efros arXiv preprint arXiv:1808.04355, 2018	147	2018
When training and test sets are different: characterizing learning transfer A Storkey Dataset shift in machine learning, 3-28, 2009	140	2009
Exploration by random network distillation Y Burda, H Edwards, A Storkey, O Klimov arXiv preprint arXiv:1810.12894, 2018	132	2018
Three factors influencing minima in sgd S Jastrzębski, Z Kenton, D Arpit, N Ballas, A Fischer, Y Bengio, A Storkey arXiv preprint arXiv:1711.04623, 2017	107	2017
Test–retest reliability of structural brain networks from diffusion MRI CR Buchanan, CR Pernet, KJ Gorgolewski, AJ Storkey, ME Bastin Neuroimage 86, 231-243, 2014	106	2014
The basins of attraction of a new Hopfield learning rule AJ Storkey, R Valabregue Neural Networks 12 (6), 869-876, 1999	100	1999
Probabilistic inference for solving (PO)MDPs M Toussaint, S Harmeling, A Storkey School of Informatics Technical Report, 2006	91	2006
TractoR: magnetic resonance imaging and tractography with R JD Clayden, SM Maniega, AJ Storkey, MD King, ME Bastin, CA Clark Journal of Statistical Software 44 (8), 1-18, 2011	87	2011
Mixture regression for covariate shift AJ Storkey, M Sugiyama Advances in Neural Information Processing Systems, 1337-1344, 2006	80*	2006
A probabilistic model-based approach to consistent white matter tract segmentation JD Clayden, AJ Storkey, ME Bastin IEEE transactions on medical imaging 26 (11), 1555-1561, 2007	77	2007
Single subject fMRI test–retest reliability metrics and confounding factors KJ Gorgolewski, AJ Storkey, ME Bastin, I Whittle, C Pernet Neuroimage 69, 231-243, 2013	75	2013
How to train your maml A Antoniou, H Edwards, A Storkey arXiv preprint arXiv:1810.09502, 2018	63	2018
Increasing the capacity of a Hopfield network without sacrificing functionality A Storkey International Conference on Artificial Neural Networks, 451-456, 1997	59	1997

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