| 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, 3981-3989, 2016 | 988 | 2016 |
| Predicting parameters in deep learning M Denil, B Shakibi, L Dinh, MA Ranzato, N De Freitas Advances in neural information processing systems, 2148-2156, 2013 | 881 | 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 | 492 | 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 | 246 | 2015 |
| 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 | 212 | 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 | 180 | 2014 |
| 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 | 177 | 2012 |
| Noisy activation functions C Gulcehre, M Moczulski, M Denil, Y Bengio International conference on machine learning, 3059-3068, 2016 | 168 | 2016 |
| Extraction of salient sentences from labelled documents M Denil, A Demiraj, N De Freitas arXiv preprint arXiv:1412.6815, 2014 | 139* | 2014 |
| 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 | 137* | 2017 |
| Learned optimizers that scale and generalize O Wichrowska, N Maheswaranathan, MW Hoffman, SG Colmenarejo, ... arXiv preprint arXiv:1703.04813, 2017 | 104 | 2017 |
| Overlap versus imbalance M Denil, T Trappenberg Canadian conference on artificial intelligence, 220-231, 2010 | 103 | 2010 |
| Consistency of online random forests M Denil, D Matheson, N de Freitas International Conference on Machine Learning, 2013 | 82 | 2013 |
| Acdc: A structured efficient linear layer M Moczulski, M Denil, J Appleyard, N de Freitas arXiv preprint arXiv:1511.05946, 2015 | 78 | 2015 |
| 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 | 56 | 2016 |
| Hyperbolic attention networks C Gulcehre, M Denil, M Malinowski, A Razavi, R Pascanu, KM Hermann, ... arXiv preprint arXiv:1805.09786, 2018 | 54 | 2018 |
| Toward the implementation of a quantum RBM M Denil, N De Freitas | 40 | 2011 |
| Programmable agents M Denil, SG Colmenarejo, S Cabi, D Saxton, N de Freitas arXiv preprint arXiv:1706.06383, 2017 | 39 | 2017 |
| Learning awareness models B Amos, L Dinh, S Cabi, T Rothörl, SG Colmenarejo, A Muldal, T Erez, ... arXiv preprint arXiv:1804.06318, 2018 | 25 | 2018 |
| Linear and parallel learning of markov random fields Y Mizrahi, M Denil, N De Freitas International Conference on Machine Learning, 199-207, 2014 | 24 | 2014 |
Nando de FreitasCIFAR & DeepMindVerified email at google.com
