| Sequential Monte Carlo methods in practice A Doucet, N De Freitas, N Gordon Springer Verlag, 2001 | 10315* | 2001 |
| An introduction to MCMC for machine learning C Andrieu, N De Freitas, A Doucet, MI Jordan Machine learning 50 (1), 5-43, 2003 | 2745 | 2003 |
| The unscented particle filter R Van Der Merwe, A Doucet, N De Freitas, EA Wan | 2317 | 2000 |
| Object recognition as machine translation: learning a lexicon for a fixed image vocabulary Leture Noyes in Computer Science P Duygulu, K Barnard, JFG de Freitas Heidelberg: Springer 23 (53), 97-112, 2002 | 2153* | 2002 |
| Matching words and pictures K Barnard, P Duygulu, D Forsyth, N De Freitas, DM Blei, MI Jordan Test accounts, 2003 | 2017 | 2003 |
| Taking the human out of the loop: A review of Bayesian optimization B Shahriari, K Swersky, Z Wang, RP Adams, N De Freitas Proceedings of the IEEE 104 (1), 148-175, 2015 | 1847 | 2015 |
| Rao-Blackwellised particle filtering for dynamic Bayesian networks K Murphy, S Russell Sequential Monte Carlo methods in practice, 499-515, 2001 | 1755 | 2001 |
| A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning E Brochu, VM Cora, N De Freitas arXiv preprint arXiv:1012.2599, 2010 | 1651 | 2010 |
| Dueling network architectures for deep reinforcement learning Z Wang, T Schaul, M Hessel, H Hasselt, M Lanctot, N Freitas International conference on machine learning, 1995-2003, 2016 | 1642 | 2016 |
| A boosted particle filter: Multitarget detection and tracking K Okuma, A Taleghani, N De Freitas, JJ Little, DG Lowe European conference on computer vision, 28-39, 2004 | 1429 | 2004 |
| Learning to learn by gradient descent by gradient descent M Andrychowicz, M Denil, S Gomez, MW Hoffman, D Pfau, T Schaul, ... arXiv preprint arXiv:1606.04474, 2016 | 1094 | 2016 |
| Predicting parameters in deep learning M Denil, B Shakibi, L Dinh, MA Ranzato, N De Freitas arXiv preprint arXiv:1306.0543, 2013 | 954 | 2013 |
| Learning to communicate with deep multi-agent reinforcement learning JN Foerster, YM Assael, N De Freitas, S Whiteson arXiv preprint arXiv:1605.06676, 2016 | 705 | 2016 |
| Sample efficient actor-critic with experience replay Z Wang, V Bapst, N Heess, V Mnih, R Munos, K Kavukcuoglu, ... arXiv preprint arXiv:1611.01224, 2016 | 468 | 2016 |
| Neural programmer-interpreters S Reed, N De Freitas arXiv preprint arXiv:1511.06279, 2015 | 364 | 2015 |
| A statistical model for general contextual object recognition P Carbonetto, N De Freitas, K Barnard European conference on computer vision, 350-362, 2004 | 349 | 2004 |
| 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 | 274 | 2015 |
| Rao-Blackwellised particle filtering for fault diagnosis N De Freitas Proceedings, IEEE Aerospace Conference 4, 4-4, 2002 | 270 | 2002 |
| Robust visual tracking for multiple targets Y Cai, N de Freitas, JJ Little European conference on computer vision, 107-118, 2006 | 266 | 2006 |
| Sequential Monte Carlo methods to train neural network models JFG Freitas, M Niranjan, AH Gee, A Doucet Neural computation 12 (4), 955-993, 2000 | 259 | 2000 |
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