| Learning to learn by gradient descent by gradient descent M Andrychowicz, M Denil, S Gomez, MW Hoffman, D Pfau, T Schaul, ... Advances in neural information processing systems 29, 2016 | 2513 | 2016 |
| Predicting parameters in deep learning M Denil, B Shakibi, L Dinh, MA Ranzato, N De Freitas Advances in neural information processing systems 26, 2013 | 1688 | 2013 |
| Learning to navigate in complex environments P Mirowski, R Pascanu, F Viola, H Soyer, AJ Ballard, A Banino, M Denil, ... International Conference on Learning Representations, 2017 | 1054 | 2017 |
| From group to individual labels using deep features D Kotzias, M Denil, N De Freitas, P Smyth Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015 | 392 | 2015 |
| Noisy activation functions C Gulcehre, M Moczulski, M Denil, Y Bengio International conference on machine learning, 3059-3068, 2016 | 369 | 2016 |
| Learning to Learn without Gradient Descent by Gradient Descent NF Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil ... International Conference on Machine Learning, 2017 | 363 | 2017 |
| Deep fried convnets Z Yang, M Moczulski, M Denil, N De Freitas, A Smola, L Song, Z Wang Proceedings of the IEEE international conference on computer vision, 1476-1483, 2015 | 360 | 2015 |
| Narrowing the gap: Random forests in theory and in practice M Denil, D Matheson, N De Freitas International conference on machine learning, 665-673, 2014 | 339 | 2014 |
| Learned optimizers that scale and generalize O Wichrowska, N Maheswaranathan, MW Hoffman, SG Colmenarejo, ... International conference on machine learning, 3751-3760, 2017 | 337 | 2017 |
| Hyperbolic attention networks C Gulcehre, M Denil, M Malinowski, A Razavi, R Pascanu, KM Hermann, ... arXiv preprint arXiv:1805.09786, 2018 | 295 | 2018 |
| Learning where to attend with deep architectures for image tracking M Denil, L Bazzani, H Larochelle, N de Freitas Neural computation 24 (8), 2151-2184, 2012 | 274 | 2012 |
| Extraction of salient sentences from labelled documents M Denil, A Demiraj, N De Freitas arXiv preprint arXiv:1412.6815, 2014 | 254* | 2014 |
| Large-scale visual speech recognition B Shillingford, Y Assael, MW Hoffman, T Paine, C Hughes, U Prabhu, ... arXiv preprint arXiv:1807.05162, 2018 | 212 | 2018 |
| Overlap versus imbalance M Denil, T Trappenberg Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial …, 2010 | 205 | 2010 |
| Scaling data-driven robotics with reward sketching and batch reinforcement learning S Cabi, SG Colmenarejo, A Novikov, K Konyushkova, S Reed, R Jeong, ... arXiv preprint arXiv:1909.12200, 2019 | 158 | 2019 |
| Acdc: A structured efficient linear layer M Moczulski, M Denil, J Appleyard, N de Freitas arXiv preprint arXiv:1511.05946, 2015 | 134 | 2015 |
| Robocat: A self-improving generalist agent for robotic manipulation K Bousmalis, G Vezzani, D Rao, C Devin, AX Lee, M Bauzá, T Davchev, ... arXiv preprint arXiv:2306.11706, 2023 | 122* | 2023 |
| Consistency of online random forests M Denil, D Matheson, N de Freitas International Conference on Machine Learning, 2013 | 119 | 2013 |
| Making efficient use of demonstrations to solve hard exploration problems TL Paine, C Gulcehre, B Shahriari, M Denil, M Hoffman, H Soyer, ... arXiv preprint arXiv:1909.01387, 2019 | 101 | 2019 |
| Learning to perform physics experiments via deep reinforcement learning M Denil, P Agrawal, TD Kulkarni, T Erez, P Battaglia, N De Freitas arXiv preprint arXiv:1611.01843, 2016 | 91 | 2016 |
Nando de FreitasCIFAR & DeepMindVerified email at google.com
