Single-channel speech separation using sparse non-negative matrix factorization. MN Schmidt, RK Olsson Interspeech 2, 2-5, 2006 | 503 | 2006 |
Bayesian non-negative matrix factorization MN Schmidt, O Winther, L Hansen Independent Component Analysis and Signal Separation, 540-547, 2009 | 281 | 2009 |
Deep learning spectroscopy: Neural networks for molecular excitation spectra K Ghosh, A Stuke, M Todorović, PB Jørgensen, MN Schmidt, A Vehtari, ... Advanced science 6 (9), 1801367, 2019 | 245 | 2019 |
Nonnegative matrix factor 2-D deconvolution for blind single channel source separation MN Schmidt, M Mørup Independent Component Analysis and Blind Signal Separation, 700-707, 2006 | 218 | 2006 |
Wind noise reduction using non-negative sparse coding MN Schmidt, J Larsen, FT Hsiao 2007 IEEE workshop on machine learning for signal processing, 431-436, 2007 | 211 | 2007 |
Nonlinear impairment compensation using expectation maximization for dispersion managed and unmanaged PDM 16-QAM transmission D Zibar, O Winther, N Franceschi, R Borkowski, A Caballero, V Arlunno, ... Optics express 20 (26), B181-B196, 2012 | 119 | 2012 |
Machine learning-based screening of complex molecules for polymer solar cells PB Jørgensen, M Mesta, S Shil, JM García Lastra, KW Jacobsen, ... The Journal of chemical physics 148 (24), 2018 | 115 | 2018 |
Nonnegative matrix factorization with Gaussian process priors MN Schmidt, H Laurberg Computational intelligence and neuroscience 2008 (1), 361705, 2008 | 107 | 2008 |
Neural message passing with edge updates for predicting properties of molecules and materials PB Jørgensen, KW Jacobsen, MN Schmidt arXiv preprint arXiv:1806.03146, 2018 | 99 | 2018 |
Nonparametric Bayesian modeling of complex networks: An introduction MN Schmidt, M Morup IEEE Signal Processing Magazine 30 (3), 110-128, 2013 | 99 | 2013 |
Unmixing of Hyperspectral Images using Bayesian Non-negative Matrix Factorization with Volume Prior M Arngren, MN Schmidt, J Larsen Journal of Signal Processing Systems, 1-18, 2010 | 98 | 2010 |
Deep generative models for molecular science PB Jørgensen, MN Schmidt, O Winther Molecular informatics 37 (1-2), 1700133, 2018 | 86 | 2018 |
Infinite multiple membership relational modeling for complex networks M Mørup, MN Schmidt, LK Hansen Machine Learning for Signal Processing (MLSP), 2011 IEEE International …, 2011 | 79 | 2011 |
Bayesian community detection M Mørup, MN Schmidt Neural computation 24 (9), 2434-2456, 2012 | 73 | 2012 |
Shift invariant sparse coding of image and music data M Mørup, MN Schmidt, LK Hansen Technical Report, 2008 | 64 | 2008 |
Completely random measures for modelling block-structured sparse networks T Herlau, MN Schmidt, M Mørup Advances in Neural Information Processing Systems 29, 2016 | 56 | 2016 |
Function factorization using warped Gaussian processes MN Schmidt Proceedings of the 26th Annual International Conference on Machine Learning …, 2009 | 50 | 2009 |
Sparse non-negative matrix factor 2-D deconvolution M Mørup, MN Schmidt | 48 | 2006 |
Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks J Busk, PB Jørgensen, A Bhowmik, MN Schmidt, O Winther, T Vegge Machine Learning: Science and Technology 3 (1), 015012, 2021 | 43 | 2021 |
Probabilistic non-negative tensor factorisation using Markov Chain Monte Carlo M Schmidt, S Mohamed European Signal Processing Conference (EUSIPCO), 2009 | 41 | 2009 |