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 | 2009 | 2021 |
Exploration by random network distillation Y Burda, H Edwards, A Storkey, O Klimov arXiv preprint arXiv:1810.12894, 2018 | 1361 | 2018 |
Data augmentation generative adversarial networks A Antoniou, A Storkey, H Edwards arXiv preprint arXiv:1711.04340, 2017 | 1288 | 2017 |
How to train your MAML A Antoniou, H Edwards, A Storkey International conference on learning representations, 2018 | 867 | 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 | 838 | 2018 |
Censoring Representations with an Adversary H Edwards, A Storkey Proceedings of ICLR and arXiv preprint arXiv:1511.05897, 2015 | 584 | 2015 |
Towards a neural statistician H Edwards, A Storkey arXiv preprint arXiv:1606.02185, 2016 | 515 | 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 | 492 | 2017 |
When training and test sets are different: characterizing learning transfer A Storkey | 438 | 2008 |
Cinic-10 is not imagenet or cifar-10 LN Darlow, EJ Crowley, A Antoniou, AJ Storkey arXiv preprint arXiv:1810.03505, 2018 | 406 | 2018 |
Neural architecture search without training J Mellor, J Turner, A Storkey, EJ Crowley International conference on machine learning, 7588-7598, 2021 | 385 | 2021 |
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 | 368 | 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 | 295 | 2006 |
Zero-shot knowledge transfer via adversarial belief matching P Micaelli, AJ Storkey Advances in Neural Information Processing Systems 32, 2019 | 228 | 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 | 183 | 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 | 161 | 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 | 158* | 2020 |
Moonshine: Distilling with cheap convolutions EJ Crowley, G Gray, AJ Storkey Advances in Neural Information Processing Systems 31, 2018 | 139 | 2018 |
Mixture regression for covariate shift AJ Storkey, M Sugiyama Advances in Neural Information Processing Systems, 1337-1344, 2006 | 139* | 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 | 120 | 2018 |