Amos Storkey

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Antreas AntoniouUniversity of EdinburghVerified email at antreas.io
Harrison EdwardsOpenAIVerified email at openai.com
Elliot J. CrowleyLecturer (Assistant Professor), University of EdinburghVerified email at ed.ac.uk
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
Marc ToussaintProfessor of Computer Science, TU Berlin, GermanyVerified email at tu-berlin.de
Asja FischerProfessor for Machine Learning, Ruhr University BochumVerified email at ini.rub.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	1380	2021
Data augmentation generative adversarial networks A Antoniou, A Storkey, H Edwards arXiv preprint arXiv:1711.04340, 2017	1093	2017
Exploration by random network distillation Y Burda, H Edwards, A Storkey, O Klimov arXiv preprint arXiv:1810.12894, 2018	1086	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	723	2018
How to train your MAML A Antoniou, H Edwards, A Storkey arXiv preprint arXiv:1810.09502, 2018	703	2018
Censoring Representations with an Adversary H Edwards, A Storkey Proceedings of ICLR and arXiv preprint arXiv:1511.05897, 2015	527	2015
Towards a neural statistician H Edwards, A Storkey arXiv preprint arXiv:1606.02185, 2016	466	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	431	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	418	2017
When training and test sets are different: characterizing learning transfer A Storkey Dataset shift in machine learning 30 (3-28), 6, 2009	385	2009
Cinic-10 is not imagenet or cifar-10 LN Darlow, EJ Crowley, A Antoniou, AJ Storkey arXiv preprint arXiv:1810.03505, 2018	277	2018
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	269	2006
Neural architecture search without training J Mellor, J Turner, A Storkey, EJ Crowley International Conference on Machine Learning, 7588-7598, 2021	254	2021
Zero-shot knowledge transfer via adversarial belief matching P Micaelli, AJ Storkey Advances in Neural Information Processing Systems 32, 2019	185	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	178	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	157	2014
Mixture regression for covariate shift AJ Storkey, M Sugiyama Advances in Neural Information Processing Systems, 1337-1344, 2006	133*	2006
Moonshine: Distilling with cheap convolutions EJ Crowley, G Gray, AJ Storkey Advances in Neural Information Processing Systems 31, 2018	124	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	121*	2020
The basins of attraction of a new Hopfield learning rule AJ Storkey, R Valabregue Neural Networks 12 (6), 869-876, 1999	115	1999

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