Amos Storkey

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Antreas AntoniouUniversity of EdinburghVerified email at antreas.io
Elliot J. CrowleyAssociate Professor, University 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
Paul MicaelliPhD Student, University of EdinburghVerified email at ed.ac.uk
Jonathan D. ClaydenAssociate Professor of Neuroimaging and Biophysics, UCL GOS Institute of Child HealthVerified email at ucl.ac.uk
Krzysztof J. GorgolewskiAnthropicVerified email at anthropic.com
Cyril PernetSenior RSE, Neurobiology Research Unit, Copenhagen University Hospital, RigshospitaletVerified email at nru.dk
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 JastrzębskiCTO & Chief Scientist @ Molecule.One, Area Chair @ ICLR '25Verified email at molecule.one
Luke DarlowSakana AIVerified email at sakana.ai
Chris WilliamsProfessor of Machine Learning, University of EdinburghVerified email at inf.ed.ac.uk
Massimiliano PatacchiolaTools for Humanity, Senior AI Research EngineerVerified email at toolsforhumanity.com
Timothy M. HospedalesProfessor, University of Edinburgh; Samsung AI ResearchVerified email at ed.ac.uk
Susana Muñoz ManiegaResearch Fellow, University of EdinburghVerified email at ed.ac.uk
David P. ReichertGoogle DeepMindVerified email at google.com

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 Bayesian Methods Machine Learning Systems


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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	2528	2021
Exploration by random network distillation Y Burda, H Edwards, A Storkey, O Klimov arXiv preprint arXiv:1810.12894, 2018	1592	2018
Data Augmentation Generative Adversarial Networks A Antoniou arXiv preprint arXiv:1711.04340, 2017	1466	2017
How to train your MAML A Antoniou, H Edwards, A Storkey International conference on learning representations, 2018	985	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	918	2018
Censoring Representations with an Adversary H Edwards, A Storkey Proceedings of ICLR and arXiv preprint arXiv:1511.05897, 2015	634	2015
Towards a neural statistician H Edwards, A Storkey arXiv preprint arXiv:1606.02185, 2016	542	2016
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	533	2017
Cinic-10 is not imagenet or cifar-10 LN Darlow, EJ Crowley, A Antoniou, AJ Storkey arXiv preprint arXiv:1810.03505, 2018	498	2018
Neural architecture search without training J Mellor, J Turner, A Storkey, EJ Crowley International conference on machine learning, 7588-7598, 2021	497	2021
When training and test sets are different: characterizing learning transfer A Storkey	477	2008
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	377	2006
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	306	2006
Zero-shot knowledge transfer via adversarial belief matching P Micaelli, AJ Storkey Advances in Neural Information Processing Systems 32, 2019	270	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	194	2014
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	178*	2020
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	168	2014
Moonshine: Distilling with cheap convolutions EJ Crowley, G Gray, AJ Storkey Advances in Neural Information Processing Systems 31, 2018	151	2018
Mixture regression for covariate shift AJ Storkey, M Sugiyama Advances in Neural Information Processing Systems, 1337-1344, 2006	145*	2006
On the relation between the sharpest directions of DNN loss and the SGD step length S Jastrzębski, Z Kenton, N Ballas, A Fischer, Y Bengio, A Storkey arXiv preprint arXiv:1807.05031, 2018	137	2018

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