Andreas Munk
TitleCited byYear
Efficient probabilistic inference in the quest for physics beyond the standard model
AG Baydin, L Shao, W Bhimji, L Heinrich, S Naderiparizi, A Munk, J Liu, ...
Advances in Neural Information Processing Systems, 5460-5473, 2019
72019
Etalumis: bringing probabilistic programming to scientific simulators at scale
AG Baydin, L Shao, W Bhimji, L Heinrich, L Meadows, J Liu, A Munk, ...
Proceedings of the International Conference for High Performance Computing†…, 2019
62019
Semi-supervised sleep-stage scoring based on single channel EEG
AM Munk, KV Olesen, SW Gangstad, LK Hansen
2018 IEEE International Conference on Acoustics, Speech and Signal†…, 2018
32018
Deep probabilistic surrogate networks for universal simulator approximation
A Munk, A Ścibior, AG Baydin, A Stewart, G Fernlund, A Poursartip, ...
arXiv preprint arXiv:1910.11950, 2019
12019
Amortized rejection sampling in universal probabilistic programming
S Naderiparizi, A Ścibior, A Munk, M Ghadiri, AG Baydin, B Gram-Hansen, ...
arXiv preprint arXiv:1910.09056, 2019
12019
Attention for Inference Compilation
W Harvey, A Munk, AG Baydin, A Bergholm, F Wood
arXiv preprint arXiv:1910.11961, 2019
2019
Efficient Bayesian Inference for Nested Simulators
B Gram-Hansen, CS de Witt, R Zinkov, S Naderiparizi, A Scibior, A Munk, ...
2019
Acoustic levitation of particles
AM Munk
2016
BAYESIAN TRANSFER LEARNING FOR DEEP NETWORKS
J Wohlert, AM Munk, S Sengupta, F Laumann
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Articles 1–9