| Normalizing Flows for Probabilistic Modeling and Inference G Papamakarios, E Nalisnick, DJ Rezende, S Mohamed, ... Journal of Machine Learning Research 22 (57), 1-64, 2021 | 1693 | 2021 |
| Masked autoregressive flow for density estimation G Papamakarios, T Pavlakou, I Murray Advances in neural information processing systems 30, 2017 | 1504 | 2017 |
| Gemini: A family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 1490 | 2023 |
| Neural spline flows C Durkan, A Bekasov, I Murray, G Papamakarios Advances in Neural Information Processing Systems, 7511-7522, 2019 | 780 | 2019 |
| Fast ε-free inference of simulation models with Bayesian conditional density estimation G Papamakarios, I Murray Advances in Neural Information Processing Systems, 1028-1036, 2016 | 410 | 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 | 358 | 2019 |
| The DeepMind JAX Ecosystem I Babuschkin, K Baumli, A Bell, S Bhupatiraju, J Bruce, P Buchlovsky, ... URL http://github. com/deepmind, 2020 | 177* | 2020 |
| Normalizing flows on tori and spheres DJ Rezende, G Papamakarios, S Racaniere, M Albergo, G Kanwar, ... International Conference on Machine Learning, 8083-8092, 2020 | 160 | 2020 |
| Temporal difference variational auto-encoder K Gregor, G Papamakarios, F Besse, L Buesing, T Weber arXiv preprint arXiv:1806.03107, 2018 | 149 | 2018 |
| The lipschitz constant of self-attention H Kim, G Papamakarios, A Mnih International Conference on Machine Learning, 5562-5571, 2021 | 145 | 2021 |
| On contrastive learning for likelihood-free inference C Durkan, I Murray, G Papamakarios International Conference on Machine Learning, 2771-2781, 2020 | 124 | 2020 |
| nflows: normalizing flows in PyTorch C Durkan, A Bekasov, I Murray, G Papamakarios Version v0 14 (10.5281), 2020 | 96* | 2020 |
| Cubic-Spline Flows C Durkan, A Bekasov, I Murray, G Papamakarios arXiv preprint arXiv:1906.02145, 2019 | 85 | 2019 |
| Targeted free energy estimation via learned mappings P Wirnsberger, AJ Ballard, G Papamakarios, S Abercrombie, S Racanière, ... The Journal of Chemical Physics 153 (14), 2020 | 79 | 2020 |
| A generalist neural algorithmic learner B Ibarz, V Kurin, G Papamakarios, K Nikiforou, M Bennani, R Csordás, ... Learning on Graphs Conference, 2: 1-2: 23, 2022 | 60 | 2022 |
| Neural density estimation and likelihood-free inference G Papamakarios arXiv preprint arXiv:1910.13233, 2019 | 51 | 2019 |
| Normalizing flows for atomic solids P Wirnsberger, G Papamakarios, B Ibarz, S Racaniere, AJ Ballard, ... Machine Learning: Science and Technology 3 (2), 025009, 2022 | 38 | 2022 |
| Causally Correct Partial Models for Reinforcement Learning DJ Rezende, I Danihelka, G Papamakarios, NR Ke, R Jiang, T Weber, ... arXiv preprint arXiv:2002.02836, 2020 | 34 | 2020 |
| Sequential Neural Methods for Likelihood-free Inference C Durkan, G Papamakarios, I Murray arXiv preprint arXiv:1811.08723, 2018 | 28 | 2018 |
| Compositional Score Modeling for Simulation-Based Inference T Geffner, G Papamakarios, A Mnih | 22* | 2023 |
Iain MurraySchool of Informatics, University of EdinburghVerified email at ed.ac.uk
