Amos Storkey

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Antreas AntoniouUniversity of EdinburghVerified email at antreas.io
Elliot J. CrowleyUniversity of EdinburghVerified email at ed.ac.uk
Harrison EdwardsOpenAIVerified email at openai.com
Michael O'BoyleProfessor of Computer Science, University of EdinburghVerified email at inf.ed.ac.uk
Jack TurnerQualcommVerified email at qti.qualcomm.com
Jonathan D. ClaydenAssociate Professor of Neuroimaging and Biophysics, UCL GOS Institute of Child HealthVerified 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
Paul MicaelliPhD Student, University of EdinburghVerified email at ed.ac.uk
Asja FischerProfessor for Machine Learning, Ruhr University BochumVerified email at ini.rub.de
Marc ToussaintProfessor of Computer Science, TU Berlin, GermanyVerified email at tu-berlin.de
Yuri BurdaOpenAIVerified email at openai.com
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
Stanisław JastrzębskiChief Technology Officer & Chief Scientist @ Molecule.OneVerified email at molecule.one
Chris WilliamsProfessor of Machine Learning, University of EdinburghVerified email at inf.ed.ac.uk
Massimiliano PatacchiolaUniversity of Cambridge, Postdoctoral ResearcherVerified email at cam.ac.uk
Luke DarlowHuawei Research Centre, EdinburghVerified email at sms.ed.ac.uk
Susana Muñoz ManiegaResearch Fellow, University of EdinburghVerified email at ed.ac.uk
David P. ReichertGoogle DeepMindVerified email at google.com
Timothy M. HospedalesProfessor, University of Edinburgh; Samsung AI ResearchVerified email at 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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Meta-learning in neural networks: A survey T Hospedales, A Antoniou, P Micaelli, A Storkey IEEE transactions on pattern analysis and machine intelligence 44 (9), 5149-5169, 2021	1516	2021
Exploration by random network distillation Y Burda, H Edwards, A Storkey, O Klimov arXiv preprint arXiv:1810.12894, 2018	1158	2018
Data augmentation generative adversarial networks A Antoniou, A Storkey, H Edwards arXiv preprint arXiv:1711.04340, 2017	1138	2017
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	755	2018
How to train your MAML A Antoniou, H Edwards, A Storkey arXiv preprint arXiv:1810.09502, 2018	748	2018
Censoring Representations with an Adversary H Edwards, A Storkey Proceedings of ICLR and arXiv preprint arXiv:1511.05897, 2015	544	2015
Towards a neural statistician H Edwards, A Storkey arXiv preprint arXiv:1606.02185, 2016	481	2016
The 2005 pascal visual object classes challenge M Everingham, A Zisserman, CKI Williams, L Van Gool, M Allan, ... Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual …, 2006	437	2006
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	435	2017
When training and test sets are different: characterizing learning transfer A Storkey Dataset shift in machine learning 30 (3-28), 6, 2009	400	2009
Cinic-10 is not imagenet or cifar-10 LN Darlow, EJ Crowley, A Antoniou, AJ Storkey arXiv preprint arXiv:1810.03505, 2018	300	2018
Neural architecture search without training J Mellor, J Turner, A Storkey, EJ Crowley International Conference on Machine Learning, 7588-7598, 2021	283	2021
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	276	2006
Zero-shot knowledge transfer via adversarial belief matching P Micaelli, AJ Storkey Advances in Neural Information Processing Systems 32, 2019	195	2019
Training deep convolutional neural networks to play go C Clark, A Storkey arXiv:1412.3409, 2014, and Proceedings of the 32nd International Conference …, 2014	179	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	158	2014
Mixture regression for covariate shift AJ Storkey, M Sugiyama Advances in Neural Information Processing Systems, 1337-1344, 2006	135*	2006
Moonshine: Distilling with cheap convolutions EJ Crowley, G Gray, AJ Storkey Advances in Neural Information Processing Systems 31, 2018	129	2018
Bayesian meta-learning for the few-shot setting via deep kernels M Patacchiola, J Turner, EJ Crowley, M O'Boyle, AJ Storkey Advances in Neural Information Processing Systems 33, 16108-16118, 2020	127*	2020
The basins of attraction of a new Hopfield learning rule AJ Storkey, R Valabregue Neural Networks 12 (6), 869-876, 1999	117	1999

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