George Papamakarios
George Papamakarios
Google DeepMind
Verified email at - Homepage
Cited by
Cited by
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
Masked autoregressive flow for density estimation
G Papamakarios, T Pavlakou, I Murray
Advances in neural information processing systems 30, 2017
Neural spline flows
C Durkan, A Bekasov, I Murray, G Papamakarios
Advances in Neural Information Processing Systems, 7511-7522, 2019
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
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
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
The DeepMind JAX Ecosystem
I Babuschkin, K Baumli, A Bell, S Bhupatiraju, J Bruce, P Buchlovsky, ...
URL http://github. com/deepmind, 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
Temporal difference variational auto-encoder
K Gregor, G Papamakarios, F Besse, L Buesing, T Weber
arXiv preprint arXiv:1806.03107, 2018
The lipschitz constant of self-attention
H Kim, G Papamakarios, A Mnih
International Conference on Machine Learning, 5562-5571, 2021
On contrastive learning for likelihood-free inference
C Durkan, I Murray, G Papamakarios
International Conference on Machine Learning, 2771-2781, 2020
nflows: normalizing flows in PyTorch
C Durkan, A Bekasov, I Murray, G Papamakarios
Version v0 14, 2020
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
Cubic-Spline Flows
C Durkan, A Bekasov, I Murray, G Papamakarios
arXiv preprint arXiv:1906.02145, 2019
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
Neural density estimation and likelihood-free inference
G Papamakarios
arXiv preprint arXiv:1910.13233, 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
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
Sequential Neural Methods for Likelihood-free Inference
C Durkan, G Papamakarios, I Murray
arXiv preprint arXiv:1811.08723, 2018
Neural belief states for partially observed domains
P Moreno, J Humplik, G Papamakarios, BA Pires, L Buesing, N Heess, ...
NeurIPS 2018 workshop on Reinforcement Learning under Partial Observability, 2018
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