| Masked autoregressive flow for density estimation G Papamakarios, T Pavlakou, I Murray Advances in neural information processing systems 30, 2017 | 1371 | 2017 |
| Evaluation methods for topic models HM Wallach, I Murray, R Salakhutdinov, D Mimno Proceedings of the 26th annual international conference on machine learning …, 2009 | 1250 | 2009 |
| MADE: Masked Autoencoder for Distribution Estimation M Germain, K Gregor, I Murray, H Larochelle Proceedings of the 32nd International Conference on Machine Learning, JMLR W …, 2015 | 914 | 2015 |
| The neural autoregressive distribution estimator H Larochelle, I Murray Proceedings of the fourteenth international conference on artificial …, 2011 | 693 | 2011 |
| Neural Spline Flows C Durkan, A Bekasov, I Murray, G Papamakarios arXiv preprint arXiv:1906.04032, 2019 | 689 | 2019 |
| On the quantitative analysis of deep belief networks R Salakhutdinov, I Murray Proceedings of the 25th international conference on Machine learning, 872-879, 2008 | 583 | 2008 |
| MCMC for doubly-intractable distributions I Murray, Z Ghahramani, DJC MacKay Proceedings of the 22nd Annual Conference on Uncertainty in Artificial …, 2006 | 541* | 2006 |
| Elliptical slice sampling I Murray, RP Adams, DJC MacKay Journal of Machine Learning Research W&CP 9, 541-548, 2010 | 538 | 2010 |
| Maximum likelihood training of score-based diffusion models Y Song, C Durkan, I Murray, S Ermon Advances in neural information processing systems 34, 1415-1428, 2021 | 430 | 2021 |
| Neural autoregressive distribution estimation B Uria, MA Côté, K Gregor, I Murray, H Larochelle Journal of Machine Learning Research 17 (205), 1-37, 2016 | 379 | 2016 |
| Fast ε-free inference of simulation models with bayesian conditional density estimation G Papamakarios, I Murray Advances in neural information processing systems 29, 2016 | 366 | 2016 |
| Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows G Papamakarios, D Sterratt, I Murray The 22nd international conference on artificial intelligence and statistics …, 2019 | 312 | 2019 |
| Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities RP Adams, I Murray, DJC MacKay Proceedings of the 26th annual international conference on machine learning …, 2009 | 278 | 2009 |
| BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning AC Stickland, I Murray arXiv preprint arXiv:1902.02671, 2019 | 277 | 2019 |
| Slice sampling covariance hyperparameters of latent Gaussian models I Murray, RP Adams Advances in neural information processing systems 23, 2010 | 270 | 2010 |
| RNADE: The real-valued neural autoregressive density-estimator B Uria, I Murray, H Larochelle Advances in Neural Information Processing Systems 26, 2013 | 266 | 2013 |
| Multiplicative LSTM for sequence modelling B Krause, L Lu, I Murray, S Renals arXiv preprint arXiv:1609.07959, 2016 | 243 | 2016 |
| A Framework for Evaluating Approximation Methods for Gaussian Process Regression K Chalupka, CKI Williams, I Murray Journal of Machine Learning Research 14, 333-350, 2013 | 208 | 2013 |
| A deep and tractable density estimator B Uria, I Murray, H Larochelle Proceedings of The 31st International Conference on Machine Learning, JMLR W …, 2014 | 183 | 2014 |
| Dynamic evaluation of neural sequence models B Krause, E Kahembwe, I Murray, S Renals International Conference on Machine Learning, 2766-2775, 2018 | 142 | 2018 |
George PapamakariosGoogle DeepMind在 google.com 的电子邮件经过验证
