Andreas M. Munk

selected publications

1. NeurIPS

   Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

   Baydin, Atilim Gunes, Shao, Lei, Bhimji, Wahid, Heinrich, Lukas, Naderiparizi, Saeid, Munk, Andreas, Liu, Jialin, Gram-Hansen, Bradley, Louppe, Gilles, Meadows, Lawrence, Torr, Philip, Lee, Victor, Cranmer, Kyle, Prabhat, Mr., and Wood, Frank

   2019

   Bib

   @incollection{baydin2019efficient,
     title = {Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model},
     booktitle = {Advances in Neural Information Processing Systems 32},
     author = {Baydin, Atilim Gunes and Shao, Lei and Bhimji, Wahid and Heinrich, Lukas and Naderiparizi, Saeid and Munk, Andreas and Liu, Jialin and {Gram-Hansen}, Bradley and Louppe, Gilles and Meadows, Lawrence and Torr, Philip and Lee, Victor and Cranmer, Kyle and Prabhat, Mr. and Wood, Frank},
     year = {2019},
     pages = {5459--5472},
     publisher = {Curran Associates, Inc.},
     copyright = {All rights reserved},
     selected = {true},
     abbr = {NeurIPS}
   }
   

2. Deep Probabilistic Surrogate Networks for Universal Simulator Approximation

   Munk, Andreas, Ścibior, Adam, Baydin, Atılım Güneş, Stewart, Andrew, Fernlund, Goran, Poursartip, Anoush, and Wood, Frank

   arXiv:1910.11950 [cs, stat] 2019

   Bib Abs

   @article{munk2019deep,
     title = {Deep Probabilistic Surrogate Networks for Universal Simulator Approximation},
     author = {Munk, Andreas and {\'S}cibior, Adam and Baydin, At{\i}l{\i}m G{\"u}ne{\c s} and Stewart, Andrew and Fernlund, Goran and Poursartip, Anoush and Wood, Frank},
     year = {2019},
     month = oct,
     archiveprefix = {arXiv},
     copyright = {All rights reserved},
     eprint = {1910.11950},
     eprinttype = {arxiv},
     journal = {arXiv:1910.11950 [cs, stat]},
     keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
     primaryclass = {cs, stat},
     selected = {true}
   }
   

   We present a framework for automatically structuring and training fast, approximate, deep neural surrogates of existing stochastic simulators. Unlike traditional approaches to surrogate modeling, our surrogates retain the interpretable structure of the reference simulators. The particular way we achieve this allows us to replace the reference simulator with the surrogate when undertaking amortized inference in the probabilistic programming sense. The fidelity and speed of our surrogates allow for not only faster "forward" stochastic simulation but also for accurate and substantially faster inference. We support these claims via experiments that involve a commercial composite-materials curing simulator. Employing our surrogate modeling technique makes inference an order of magnitude faster, opening up the possibility of doing simulator-based, non-invasive, just-in-time parts quality testing; in this case inferring safety-critical latent internal temperature profiles of composite materials undergoing curing from surface temperature profile measurements.

1. ICASSP

   Semi-Supervised Sleep-Stage Scoring Based on Single Channel EEG

   Munk, A. M., Olesen, K. V., Gangstad, S. W., and Hansen, L. K.

   In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2018

   Bib Abs

   @inproceedings{munk2018semisupervised,
     title = {Semi-Supervised Sleep-Stage Scoring Based on Single Channel EEG},
     booktitle = {2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
     author = {Munk, A. M. and Olesen, K. V. and Gangstad, S. W. and Hansen, L. K.},
     year = {2018},
     month = apr,
     pages = {2551--2555},
     issn = {2379-190X},
     doi = {10.1109/ICASSP.2018.8461982},
     copyright = {All rights reserved},
     keywords = {automatic sleep stage classification,Brain modeling,EEG,electroencephalography,Electroencephalography,fully-supervised model,Gaussian mixture model,generalizable Gaussian mixture model,learning (artificial intelligence),medical signal processing,non-negative matrix factorizaton,pattern classification,semi-supervised learning,semisupervised approach performs,semisupervised sleep-stage scoring,signal classification,sleep,Sleep,sleep stage scoring,sleep stages,Spectrogram,supervised classification algorithms,supervised machine learning algorithms,Training},
     abbr = {ICASSP},
     selected = {true}
   }
   

   The field of automatic sleep stage classification based on EEG has enjoyed substantial attention during the last decade, which has resulted in several supervised classification algorithms with highly encouraging performance. Such supervised machine learning algorithms require large training sets that have been manually labelled, and are time- and resource-consuming to acquire. Here we present a semi-supervised approach that can learn to distinguish the sleep stages from a one-night data set where only a fraction has been manually labelled. We show that for fractions larger than 50%, our semi-supervised approach performs as good as a similar, fully-supervised model.