Amos Storkey

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Citations	7083	5464
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Harrison EdwardsOpenAIVerified email at openai.com
Antreas AntoniouUniversity of EdinburghVerified email at sms.ed.ac.uk
Elliot J. CrowleyLecturer in Machine Learning and Computer Vision, University of EdinburghVerified email at ed.ac.uk
Jonathan D. ClaydenLecturer in Neuroimaging and Biophysics, UCL GOS Institute of Child Health, University CollegeVerified email at ucl.ac.uk
Krzysztof J. GorgolewskiGoogleVerified email at google.com
Cyril PernetSenior RSE, Neurobiology Research Unit, Copenhagen University Hospital, RigshospitaletVerified email at nru.dk
Marc ToussaintProfessor of Computer Science, TU Berlin, GermanyVerified email at tu-berlin.de
Michael O'BoyleProfessor of Computer Science, University of EdinburghVerified email at inf.ed.ac.uk
Jack TurnerReservoir LabsVerified email at reservoir.com
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
Asja FischerProfessor for Machine Learning, Ruhr University BochumVerified email at ini.rub.de
Stanisław JastrzębskiCSO @ Molecule.one & Assistant Prof. @ Jagiellonian University (GMUM.net)Verified email at molecule.one
Chris WilliamsProfessor of Machine Learning, University of EdinburghVerified email at inf.ed.ac.uk
Yuri BurdaOpenAIVerified email at openai.com
Susana Muñoz ManiegaResearch Fellow, University of EdinburghVerified email at ed.ac.uk
David P. ReichertGoogle DeepMindVerified email at google.com
Paul MicaelliPhD Student, University of EdinburghVerified email at ed.ac.uk
Massimiliano PatacchiolaUniversity of Cambridge, Postdoctoral ResearcherVerified email at cam.ac.uk
Romain ValabregueCENIR ICMVerified email at upmc.fr
Luke DarlowUniversity of Edinburgh (CDT in Data Science)Verified email at sms.ed.ac.uk

Amos Storkey

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

Verified email at ed.ac.uk - Homepage

Machine Learning Deep Learning Meta-Learning Prediction Markets Bayesian Methods


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Data augmentation generative adversarial networks A Antoniou, A Storkey, H Edwards arXiv preprint arXiv:1711.04340, 2017	590	2017
Exploration by random network distillation Y Burda, H Edwards, A Storkey, O Klimov arXiv preprint arXiv:1810.12894, 2018	481	2018
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	388	2018
Censoring Representations with an Adversary H Edwards, A Storkey Proceedings of ICLR and arXiv preprint arXiv:1511.05897, 2015	339	2015
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	313	2005
Towards a neural statistician H Edwards, A Storkey arXiv preprint arXiv:1606.02185, 2016	308	2016
Meta-learning in neural networks: A survey T Hospedales, A Antoniou, P Micaelli, A Storkey arXiv preprint arXiv:2004.05439, 2020	289	2020
How to train your MAML A Antoniou, H Edwards, A Storkey arXiv preprint arXiv:1810.09502, 2018	275	2018
When training and test sets are different: characterizing learning transfer A Storkey Dataset shift in machine learning 30, 3-28, 2009	237	2009
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	231	2017
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	228	2006
Training deep convolutional neural networks to play go C Clark, A Storkey arXiv:1412.3409, 2014, and Proceedings of the 32nd International Conference …, 2014	151	2014
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	130	2014
Mixture regression for covariate shift AJ Storkey, M Sugiyama Advances in Neural Information Processing Systems, 1337-1344, 2006	111	2006
The basins of attraction of a new Hopfield learning rule AJ Storkey, R Valabregue Neural Networks 12 (6), 869-876, 1999	107	1999
Probabilistic inference for solving (PO)MDPs M Toussaint, S Harmeling, A Storkey School of Informatics Technical Report, 2006	106	2006
TractoR: magnetic resonance imaging and tractography with R JD Clayden, S Muñoz Maniega, AJ Storkey, MD King, ME Bastin, ... Journal of Statistical Software 44 (8), 1-18, 2011	97	2011
Cinic-10 is not imagenet or cifar-10 LN Darlow, EJ Crowley, A Antoniou, AJ Storkey arXiv preprint arXiv:1810.03505, 2018	95	2018
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	91	2013
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	84	2007

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