| A fast learning algorithm for deep belief nets GE Hinton, S Osindero, YW Teh Neural computation 18 (7), 1527-1554, 2006 | 21976 | 2006 |
| Conditional generative adversarial nets M Mirza, S Osindero NIPS 2014: Deep Learning and Representation Learning Workshop, 2014 | 14364 | 2014 |
| Training compute-optimal large language models J Hoffmann, S Borgeaud, A Mensch, E Buchatskaya, T Cai, E Rutherford, ... arXiv preprint arXiv:2203.15556, 2022 | 1758 | 2022 |
| Meta-Learning with Latent Embedding Optimization AA Rusu, D Rao, J Sygnowski, O Vinyals, R Pascanu, S Osindero, ... arXiv preprint arXiv:1807.05960, 2018 | 1671 | 2018 |
| Feudal networks for hierarchical reinforcement learning AS Vezhnevets, S Osindero, T Schaul, N Heess, M Jaderberg, D Silver, ... Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 1117 | 2017 |
| Scaling Language Models: Methods, Analysis & Insights from Training Gopher JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ... arXiv preprint arXiv:2112.11446, 2021 | 1069 | 2021 |
| Improving language models by retrieving from trillions of tokens S Borgeaud, A Mensch, J Hoffmann, T Cai, E Rutherford, K Millican, ... International Conference on Machine Learning, 2206-2240, 2022 | 1011 | 2022 |
| Population Based Training of Neural Networks M Jaderberg, V Dalibard, S Osindero, WM Czarnecki, J Donahue, ... arXiv preprint arXiv:1711.09846, 2017 | 926 | 2017 |
| The Dartmouth College artificial intelligence conference: The next fifty years J Moor Ai Magazine 27 (4), 87, 2006 | 761 | 2006 |
| Recursive Recurrent Nets with Attention Modeling for OCR in the Wild CY Lee, S Osindero Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 615 | 2016 |
| Cross-Dimensional Weighting for Aggregated Deep Convolutional Features Y Kalantidis, C Mellina, S Osindero European Conference on Computer Vision, 685-701, 2016 | 506 | 2016 |
| Decoupled neural interfaces using synthetic gradients M Jaderberg, WM Czarnecki, S Osindero, O Vinyals, A Graves, D Silver, ... Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 437 | 2017 |
| Sobolev training for neural networks WM Czarnecki, S Osindero, M Jaderberg, G Swirszcz, R Pascanu Advances in Neural Information Processing Systems, 4281-4290, 2017 | 288 | 2017 |
| Energy-based models for sparse overcomplete representations YW Teh, M Welling, S Osindero, GE Hinton Journal of Machine Learning Research 4 (Dec), 1235-1260, 2003 | 267 | 2003 |
| Learning sparse topographic representations with products of student-t distributions M Welling, S Osindero, GE Hinton Advances in neural information processing systems, 1359-1366, 2002 | 199 | 2002 |
| An alternative infinite mixture of Gaussian process experts E Meeds, S Osindero Advances in Neural Information Processing Systems 18, 883, 2006 | 191 | 2006 |
| Unsupervised discovery of nonlinear structure using contrastive backpropagation G Hinton, S Osindero, M Welling, YW Teh Cognitive Science 30 (4), 725-731, 2006 | 189 | 2006 |
| Strategic attentive writer for learning macro-actions A Vezhnevets, V Mnih, S Osindero, A Graves, O Vinyals, J Agapiou Advances in Neural Information Processing Systems, 3486-3494, 2016 | 188 | 2016 |
| Modeling image patches with a directed hierarchy of Markov random fields S Osindero, GE Hinton Advances in neural information processing systems, 1121-1128, 2008 | 160 | 2008 |
| Distilling policy distillation WM Czarnecki, R Pascanu, S Osindero, S Jayakumar, G Swirszcz, ... The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 153 | 2019 |
Geoffrey HintonEmeritus Prof. Computer Science, University of TorontoEmail verificata su cs.toronto.edu
