| Learning disentangled representations with semi-supervised deep generative models B Paige, JW Van De Meent, A Desmaison, N Goodman, P Kohli, F Wood, ... Advances in neural information processing systems 30, 2017 | 415 | 2017 |
| A new approach to probabilistic programming inference F Wood, JW Meent, V Mansinghka Artificial intelligence and statistics, 1024-1032, 2014 | 408 | 2014 |
| Deep variational reinforcement learning for POMDPs M Igl, L Zintgraf, TA Le, F Wood, S Whiteson International conference on machine learning, 2117-2126, 2018 | 335 | 2018 |
| Improved few-shot visual classification P Bateni, R Goyal, V Masrani, F Wood, L Sigal Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 305 | 2020 |
| Diagnosis code assignment: models and evaluation metrics A Perotte, R Pivovarov, K Natarajan, N Weiskopf, F Wood, N Elhadad Journal of the American Medical Informatics Association 21 (2), 231-237, 2014 | 289 | 2014 |
| Online learning rate adaptation with hypergradient descent AG Baydin, R Cornish, DM Rubio, M Schmidt, F Wood arXiv preprint arXiv:1703.04782, 2017 | 284 | 2017 |
| Flexible diffusion modeling of long videos W Harvey, S Naderiparizi, V Masrani, C Weilbach, F Wood Advances in Neural Information Processing Systems 35, 27953-27965, 2022 | 271 | 2022 |
| On the variability of manual spike sorting F Wood, MJ Black, C Vargas-Irwin, M Fellows, JP Donoghue IEEE Transactions on Biomedical Engineering 51 (6), 912-918, 2004 | 250 | 2004 |
| Tighter variational bounds are not necessarily better T Rainforth, A Kosiorek, TA Le, C Maddison, M Igl, F Wood, YW Teh International Conference on Machine Learning, 4277-4285, 2018 | 234 | 2018 |
| An introduction to probabilistic programming JW van de Meent, B Paige, H Yang, F Wood arXiv preprint arXiv:1809.10756, 2018 | 221 | 2018 |
| Auto-encoding sequential monte carlo TA Le, M Igl, T Rainforth, T Jin, F Wood arXiv preprint arXiv:1705.10306, 2017 | 195 | 2017 |
| Hierarchically supervised latent Dirichlet allocation A Perotte, F Wood, N Elhadad, N Bartlett Advances in neural information processing systems 24, 2011 | 184 | 2011 |
| Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints S Staton, H Yang, F Wood, C Heunen, O Kammar Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer …, 2016 | 181 | 2016 |
| Inference compilation and universal probabilistic programming TA Le, AG Baydin, F Wood Artificial Intelligence and Statistics, 1338-1348, 2017 | 172 | 2017 |
| On nesting monte carlo estimators T Rainforth, R Cornish, H Yang, A Warrington, F Wood International Conference on Machine Learning, 4267-4276, 2018 | 168 | 2018 |
| A nonparametric Bayesian alternative to spike sorting F Wood, MJ Black Journal of neuroscience methods 173 (1), 1-12, 2008 | 161 | 2008 |
| Design and implementation of probabilistic programming language anglican D Tolpin, JW van de Meent, H Yang, F Wood Proceedings of the 28th Symposium on the Implementation and Application of …, 2016 | 151 | 2016 |
| Using synthetic data to train neural networks is model-based reasoning TA Le, AG Baydin, R Zinkov, F Wood 2017 international joint conference on neural networks (IJCNN), 3514-3521, 2017 | 143 | 2017 |
| A stochastic memoizer for sequence data F Wood, C Archambeau, J Gasthaus, L James, YW Teh Proceedings of the 26th annual international conference on machine learning …, 2009 | 137 | 2009 |
| Inference networks for sequential Monte Carlo in graphical models B Paige, F Wood International Conference on Machine Learning, 3040-3049, 2016 | 119 | 2016 |
Jan-Willem van de MeentUniversity of Amsterdam and Northeastern University確認したメール アドレス: uva.nl
