Publications
Journal papers
- [1]
- François Cornet, Bardi
Benediktsson, Bjarke Hastrup, Mikkel N.
Schmidt, and Arghya Bhowmik.
Om-diff: inverse-design of organometallic catalysts with guided equivariant
denoising diffusion.
Digital Discovery, 2024.
(pdf)
(doi:10.1039/D4DD00099D)
- [2]
- Philip J. H. Jørgensen,
Søren F. Nielsen, Jesper L. Hinrich,
Mikkel N. Schmidt, Kristoffer H. Madsen, and
Morten Mørup.
Probabilistic parafac2.
Entropy, 2024.
(pdf)
(doi:10.3390/e26080697)
- [3]
- Bo Li,
Yasin Esfandiari, Mikkel N. Schmidt,
Tommy S. Alstrøm, and Sebastian U. Stich.
Synthetic data shuffling accelerates the convergence of federated learning
under data heterogeneity.
Transactions on Machine Learning Research (TMLR), 2024.
(pdf)
- [4]
- Jonas
Busk, Mikkel N. Schmidt, Ole Winther,
Tejs Vegge, and Peter Bjørn Jørgensen.
Graph neural network interatomic potential ensembles with calibrated aleatoric
and epistemic uncertainty on energy and forces.
Physical Chemistry Chemical Physics, 2023.
(pdf)
(doi:10.1039/D3CP02143B)
- [5]
- David Frich Hansen,
Tommy Sonne Alstrøm, and Mikkel N. Schmidt.
Probabilistic signal estimation for vibrational spectroscopy with a flexible
non-stationary gaussian process baseline model.
Chemometrics and Intelligent Laboratory Systems, 2023.
(pdf)
(doi:10.1016/j.chemolab.2023.104974)
- [6]
- Bo li,
Giulia Zappalá, Elodie Dumont,
Anja Boisen, Tomas Rindzevicius,
Mikkel N. Schmidt, and Tommy S. Alstrøm.
Nitroaromatic explosives detection and quantification using attention-based
transformer on surface-enhanced raman spectroscopy maps.
Analyst, 2023.
(pdf)
(doi:10.1039/D3AN00446E)
- [7]
- Muralikrishnan Srinivasan,
Jinxiang Song, Alexander Grabowski,
Krzysztof Szczerba, Holger K. Iversen,
Mikkel N. Schmidt, Darko Zibar,
Jochen Schrøder, Anders Larsson,
Christian Häger, and Henk Wymeersch.
End-to-end learning for vcsel-based
optical interconnects: State-of-the-art, challenges, and opportunities.
Journal of Lightwave Technology, 41, 2023.
(pdf)
(doi:10.1109/JLT.2023.3251660)
- [8]
- Kristoffer Jon Albers,
Matthew G. Liptrot, Karen Sandø Ambrosen,
Rasmus Røge, Tue Herlau,
Kasper Winther Andersen, Hartwig R. Siebner,
Lars Kai Hansen, Tim B. Dyrby,
Kristoffer H. Madsen, Mikkel N. Schmidt, and
Morten Mørup.
Uncovering cortical units of processing from multi-layered connectomes.
Frontiers in Neuroscience, 2022.
(doi:10.3389/fnins.2022.836259)
- [9]
- Bo li,
Mikkel N. Schmidt, and Tommy S. Alstrøm.
Raman spectrum matching with contrastive representation learning.
Analyst, 2022.
(pdf)
(doi:10.1039/D2AN00403H)
- [10]
- Rasmus Bonnevie and Mikkel N.
Schmidt.
Matrix product states for
inference in discrete probabilistic models.
Journal of machine learning research, 22, 2021.
(pdf)
- [11]
- Jonas
Busk, Peter Bjørn Jørgensen, Arghya
Bhowmik, Mikkel N. Schmidt, Ole Winther, and
Tejs Vegge.
Calibrated uncertainty for molecular property prediction using ensembles of
message passing neural networks.
Machine Learning: Science and Technology, 3(1), 2021.
(pdf)
(doi:10.1088/2632-2153/ac3eb3)
- [12]
- Mikkel N. Schmidt, Daniel
Seddig, Eldad Davidov, Morten Mørup,
Jan Michael Bauer, and Fumiko Kano
Glückstad.
Latent profile analysis of human values: What is the optimal number of
clusters?
Methodology, 17, 2021.
(pdf)
(doi:10.5964/meth.5479)
- [13]
- Kristoffer Jon Albers,
Karen S. Ambrosen, Matthew G. Liptrot,
Tim B. Dyrby, Mikkel N. Schmidt, and
Morten Mørup.
Using connectomics for predictive assessment of brain parcellations.
NeuroImage, 238, September 2021.
(pdf)
(doi:10.1016/j.neuroimage.2021.118170)
- [14]
- Kristoffer Jon Albers, Morten
Mørup, Mikkel N. Schmidt, and Fumiko K.
Glückstad.
Predictive evaluation of human value segmentations.
Journal of Mathematical Sociology, 2020.
(pdf)
(doi:10.1080/0022250X.2020.1811277)
- [15]
- Fumiko K. Glückstad,
Mikkel N. Schmidt, and Morten Mørup.
Testing a model of destination image formation: Application of bayesian
relational modeling and fsqca.
Journal of Business Research, 120:351–363, November 2020.
(pdf)
(doi:10.1016/j.jbusres.2019.10.014)
- [16]
- Karen S. Ambrosen, Simon F.
Eskildsen, Max Hinne, Kristine Krug,
Henrik Lundell, Mikkel N. Schmidt,
Marcel A. J. van Gerven, Morten Mørup, and
Tim B. Dyrby.
Validation of structural brain connectivity networks: The impact of scanning
parameters.
Neuroimage, 204, 2019.
(pdf)
(doi:10.1016/j.neuroimage.2019.116207)
- [17]
- Peter Bjørn Jørgensen,
Estefanıa Garijo del Rıo, Mikkel N.
Schmidt, and Karsten Wedel Jacobsen.
Materials property prediction using symmetry-labeled graphs as atomic-position
independent descriptors.
Physical Review B, 100(104114), 2019.
(pdf)
(doi:10.1103/PhysRevB.100.104114)
- [18]
- Kunal
Ghosh, Annika Stuke, Milica Todorović,
Peter Bjørn Jørgensen, Mikkel N. Schmidt,
Aki Vehtari, and Patrick Rinke.
Deep learning spectroscopy: Neural networks for molecular excitation spectra.
Advanced Science, 6, May 2019.
(pdf)
(doi:10.1002/advs.201801367)
- [19]
- Mikkel N. Schmidt and Morten
Mørup.
Efficient computation for bayesian comparison of two proportions.
Statistics & probability letters, 145:57–62, February 2019.
(pdf)
(doi:10.1016/j.spl.2018.08.011)
- [20]
- Peter Bjørn Jørgensen,
Murat Mesta, Suranjan Shil, Juan
Maria García Lastra, Karsten Wedel Jacobsen,
Kristian Sommer Thygesen, and Mikkel N.
Schmidt.
Machine learning-based screening of complex molecules for polymer solar cells.
The Journal of Chemical Physics, 148(241735), 2018.
(pdf)
(doi:10.1063/1.5023563)
- [21]
- Fumiko K. Glückstad,
Mikkel N. Schmidt, and Morten Mørup.
Examination of heterogeneous societies: Identifying subpopulations by
contrasting cultures.
Journal of Cross-Cultural Psychology, 48(1), 2017.
(pdf)
(doi:10.1177/0022022116672346)
- [22]
- Søren Føns Vind Nielsen,
Mikkel N. Schmidt, Kristoffer Hougaard Madsen,
and Morten Mørup.
Predictive assessment of models for dynamic functional connectivity.
NeuroImage, 2017.
(pdf)
(doi:10.1016/j.neuroimage.2017.12.084)
- [23]
- Rasmus E. Røge,
Kristoffer H. Madsen, Mikkel N. Schmidt, and
Morten Mørup.
Infinite von Mises-Fisher mixture modeling of whole-brain fMRI data.
Neural Computation, 29(10):2712–2741, October 2017.
(pdf)
(doi:10.1162/neco_a_01000)
- [24]
- Peter B. Jørgensen,
Mikkel N. Schmidt, and Ole Winther.
Deep generative models for molecular science.
Molecular Informatics, 37(1–2), February 2017.
(pdf)
(doi:10.1002/minf.201700133)
- [25]
- Kasper B. Frøhling,
Tommy S. Alstrøm, Michael Bache,
Michael S. Schmidt, Mikkel N. Schmidt,
Jan larsen, Mogens H. Jakobsen, and
Anja Boisen.
Surface-enhanced raman spectroscopic study of dna and 6-mercapto-1-hexanol
interactions using large area mapping.
Vibrational Spectroscopy, 86:331–336, September 2016.
(pdf)
(doi:10.1016/j.vibspec.2016.08.005)
- [26]
- Kasper Winther Andersen,
Kristoffer H. Madsen, Hartwig Roman Siebner,
Mikkel N. Schmidt, Morten Mørup, and
Lars Kai Hansen.
Non-parametric bayesian graph models reveal community structure in resting
state fmri.
NeuroImage, pages 301–15, 2014.
(pdf)
(doi:10.1016/j.neuroimage.2014.05.083)
- [27]
- Fumiko K. Glückstad, Tue
Herlau, Mikkel N. Schmidt, and Morten
Mørup.
Cross-categorization of legal concepts across boundaries of legal systems.
Artificial Intelligence and Law, 2014.
(pdf)
(doi:10.1007/s10506-013-9150-2)
- [28]
- Tue
Herlau, Mikkel N. Schmidt, and Morten
Mørup.
Infinite-degree-corrected stochastic block model.
Physical Review E, 90(032819), 2014.
(pdf)
(doi:10.1103/PhysRevE.90.032819)
- [29]
- Darko
Zibar, Ole Winther, Niccolo Franceschi,
Robert Borkowski, Antonio Caballero,
Valeria Arlunno, Mikkel N. Schmidt,
Neil Guerrero Gonzales, Bangning Mao,
Yabin Ye, Knud J. Larsen, and
Idelfonso Tafur Monroy.
Nonlinear impairment compensation using expectation maximization for dispersion
managed and unmanaged pdm 16-qam transmission.
Optics Express, 20(26):B181–B196, 2013.
(doi:10.1364/OE.20.00B181)
- [30]
- Mikkel N. Schmidt and Morten
Mørup.
Non-parametric bayesian modeling of complex networks. an introduction.
IEEE Signal Processing Magazine, 30(3):110–128, May 2013.
(pdf)
(doi:10.1109/MSP.2012.2235191)
- [31]
- Morten Mørup and Mikkel N.
Schmidt.
Bayesian community detection.
Neural Computation, 24(9):2434–56, 2012.
(pdf)
(doi:10.1162/NECO_a_00314)
- [32]
- Morten Arngren, Mikkel N.
Schmidt, and Jan Larsen.
Unmixing of hyperspectral images using bayesian nonnegative matrix
factorization with volume prior.
Journal of Signal Processing Systems, 65(3):479–496, 2010.
(pdf)
(doi:10.1007/s11265-010-0533-2)
- [33]
- Mikkel N. Schmidt and Hans
Laurberg.
Non-negative matrix factorization with gaussian process priors.
Computational Intelligence and Neuroscience, 2008.
(pdf)
(doi:10.1155/2008/361705)
Conference papers
- [1]
- François Cornet, Grigory
Bartosh, Mikkel N. Schmidt, and Christian A.
Naesseth.
Equivariant neural
diffusion for molecule generation.
In Neural Information Processing (NeurIPS), 2024.
(pdf)
- [2]
- François Cornet, Pratham
Deshmukh, Bardi Benediktsson, Mikkel N.
Schmidt, and Arghya Bhowmik.
Equivariant conditional
diffusion model for exploring the chemical space around vaska's complex.
In AI for Accelerated Materials Design, Neurips Workshop on
(AI4MAT), 2024.
(pdf)
- [3]
- Bo Li,
Xiaowen Jiang, Mikkel N. Schmidt,
Tommy S. Alstrøm, and Sebastian U. Stich.
An improved analysis of
per-sample and per-update clipping in federated learning.
In Learning Representations, International Conference on (ICLR),
2024.
(pdf)
- [4]
- Anna Emilie J. Wedenborg,
Michael Alexander Harborg, Andreas Bigom,
Oliver Elmgreen, Marcus Presuitti,
Andreas Raskov, Fumiko Kano Glückstad,
Mikkel N. Schmidt, and Morten Mørup.
Modeling human responses by ordinal archetypal analysis.
In Machine Learning for Signal Processing, IEEE International Workshop
on, (MLSP), 2024.
(pdf)
(doi:10.1109/MLSP58920.2024.10734804)
- [5]
- Thea Brüsch, Mikkel N.
Schmidt, and Tommy S. Alstrøm.
Multi-view self-supervised learning for multivariate variable-channel time
series.
In Machine Learning for Signal Processing, IEEE International Workshop
on, (MLSP), 2023.
(pdf)
(doi:10.1109/MLSP55844.2023.10285993)
- [6]
- François R. J. Cornet,
Bardi Benediktsson, Bjarke Hastrup,
Arghya Bhowmik, and Mikkel N. Schmidt.
Inverse-design of organometallic catalysts with guided equivariant diffusion.
In ELLIS Advancing Molecular Machine Learning Workshop (ML4Molecules) and
AI for Accelerated Materials Design, NeurIPS Workshop (AI4MAT), 2023.
(pdf)
- [7]
- David Frich Hansen, Tommy S.
Alstrøm, and Mikkel N. Schmidt.
Amortized variational peak fitting for spectroscopic data.
In Machine Learning for Signal Processing, IEEE International Workshop
on, (MLSP), 2023.
(pdf)
(doi:10.1109/MLSP55844.2023.10285981)
- [8]
- Peter Bjørn Jørgensen,
Jonas Busk, Ole Winther, and
Mikkel N. Schmidt.
Coherent energy and force uncertainty in deep learning force fields.
In ELLIS Advancing Molecular Machine Learning Workshop
(ML4Molecules), 2023.
(pdf)
- [9]
- Bo Li,
Mikkel N. Schmidt, Tommy S. Alstrøm, and
Sebastian U. Stich.
On
the effectiveness of partial variance reduction in federated learning with
heterogeneous data.
In Computer Vision and Pattern Recognition Conference, The IEEE/CVF
(CVPR), pages 3964–3973, 2023.
(pdf)
- [10]
- Anders S. Olsen, Emil
Ortvald, Kristoffer H. Madsen, Mikkel N.
Schmidt, and Morten Mørup.
Angular central gaussian and watson mixture models for assessing dynamic
functional brain connectivity during a motor task.
In Unraveling the Brain, Data Science and Learning Workshop (DSLW),
ICASSP Satellite, 2023.
(pdf)
(doi:10.1109/ICASSPW59220.2023.10193021)
- [11]
- Tue Herlau, Mikkel N.
Schmidt, and Morten Mørup.
Bayesian dropout.
In Workshop on Statistical Methods and Artificial Intelligence,
International Workshop on (IWSMAI), Procedia Computer Science, vol
201., pages 771–776, 2022.
(pdf)
(doi:10.1016/j.procs.2022.03.105)
- [12]
- Rasmus Larsen and Mikkel N.
Schmidt.
Programmatic policy extraction by iterative local search.
In Approaches and Applications of Inductive Programming, International
Workshop on (AAIP), Lecture Notes in Computer Science, vol 13191.,
2021.
(pdf)
(doi:10.1007/978-3-030-97454-1_11)
- [13]
- Mikkel N. Schmidt, Tommy S.
Alstrøm, Marcus Svendstorp, and Jan Larsen.
Peak detection and baseline correction using a convolutional neural network.
In Acoustics, speech and signal processing, IEEE international conference
on (ICASSP), 2019.
(pdf)
(doi:10.1109/ICASSP.2019.8682311)
- [14]
- Maximillian F. Vording,
Peter O. Okeyo, Juan J. R. Guillamon,
Peter E. Larsen, Mikkel N. Schmidt, and
Tommy S. Alstrøm.
A bayesian generative model with gaussian process priors for termomechanical
analysis of micro-resonators.
In Machine Learning for Signal Processing, IEEE International Workshop
on, (MLSP), 2019.
(pdf)
(doi:10.1109/MLSP.2019.8918876)
- [15]
- Peter Bjørn Jørgensen,
Karsten Wedel Jacobsen, and Mikkel N. Schmidt.
Neural message passing with edge updates for predicting properties of molecules
and materials.
In Machine Learning for Molecules and Materials, NIPS workshop on,
2018.
(pdf)
- [16]
- Søren F. V. Nielsen, Diego
Vidaurre, Mikkel N. Schmidt, Kristoffer H.
Madsen, and Morten Mørup.
Testing group differences in state transition structure of dynamic functional
connectivity models.
In Pattern Recognition in NeuroImaging (PRNI), 2018.
(pdf)
(doi:10.1109/PRNI.2018.8423966)
- [17]
- Kristoffer Jon Albers,
Mikkel N. Schmidt, Morten Mørup,
Marisciel Litong-Palima, Rasmus Bonnevie, and
Fumiko Kano Glückstad.
Understanding mindsets across markets, internationally: A public-private
innovation project for developing a tourist data analytic platform.
In Computer Software and Applications Conference (COMPSAC), pages
159–164, July 2018.
(pdf)
(doi:10.1109/COMPSAC.2018.10221)
- [18]
- Tommy S. Alstrøm, Mikkel N.
Schmidt, Tomas Rindzevicius, Anja Boisen, and
Jan Larsen.
A pseudo-voigt component model for high-resolution recovery of constituent
spectra in raman spectroscopy.
In Acoustics, speech and signal processing, IEEE international conference
on (ICASSP), 2017.
(pdf)
(doi:10.1109/ICASSP.2017.7952570)
- [19]
- Rasmus Bonnevie, Morten
Mørup, and Mikkel N. Schmidt.
Difference-of-convex optimization for variational kl-corrected inference in
dirichlet process mixtures.
In Machine Learning for Signal Processing, IEEE International Workshop
on, (MLSP), 2017.
(pdf)
(doi:10.1109/MLSP.2017.8168159)
- [20]
- Jesper L. Hinrich, Søren
F. V. Nielsen, Nicolai A. B. Riis, Casper T.
Eriksen, Jacob Frøsig, Marco D. F.
Kristensen, Mikkel N. Schmidt, Kristoffer H.
Madsen, and Morten Mørup.
Scalable group level probabilistic sparse factor analysis.
In Acoustics, speech and signal processing, IEEE international conference
on (ICASSP), 2017.
(pdf)
(doi:10.1109/ICASSP.2017.7952570)
- [21]
- Søren F. V. Nielsen,
Kristoffer H. Madsen, Mikkel N. Schmidt, and
Morten Mørup.
Modeling dynamic functional connectivity using a wishart mixture model.
In Pattern Recognition in NeuroImaging (PRNI), 2017.
(pdf)
(doi:10.1109/PRNI.2017.7981505)
- [22]
- Rasmus Røge, Karen Sandø
Ambrosen, Kristoffer Jon Albers,
Casper Tabassum Eriksen, Matthew George
Liptrot, Mikkel N. Schmidt, Kristoffer Hougaard
Madsen, and Morten Mørup.
Whole brain functional connectivity predicted by indirect structural
connections.
In Pattern Recognition in NeuroImaging (PRNI), 2017.
(pdf)
(doi:10.1109/PRNI.2017.7981496)
- [23]
- Kristoffer J. Albers, Morten
Mørup, and Mikkel N. Schmidt.
The influence of hyper-parameters in the infinite relational model.
In Machine Learning for Signal Processing, IEEE International Workshop
on, (MLSP), 2016.
(pdf)
(doi:10.1109/MLSP.2016.7738908)
- [24]
- Tue Herlau, Mikkel N.
Schmidt, and Morten Mørup.
Completely random measures for modelling block-structured sparse networks.
In Advances in neural information processing (NIPS), 2016.
(pdf)
- [25]
- Philip H. Jørgensen, Morten
Mørup, Mikkel N. Schmidt, and Tue Herlau.
Bayesian latent feature modeling for modeling bipartite networks with
overlapping groups.
In Machine Learning for Signal Processing, IEEE International Workshop
on, (MLSP), 2016.
(pdf)
(doi:10.1109/MLSP.2016.7738845)
- [26]
- Rasmus E. Røge,
Kristoffer H. Madsen, Mikkel N. Schmidt, and
Morten Mørup.
Unsupervised segmentation of task activated regions in fmri.
In Machine Learning for Signal Processing, IEEE International Workshop
on, (MLSP), 2015.
(pdf)
(doi:10.1109/MLSP.2015.7324384)
- [27]
- Mikkel N. Schmidt and
Kristoffer Jon Albers.
Numerical approximations for speeding up mcmc inference in the infinite
relational model.
In European Signal Processing Conference (EUSIPCO), 2015.
(pdf)
(doi:10.1109/EUSIPCO.2015.7362891)
- [28]
- Tommy S. Alstrøm, Kasper B.
Frøhling, Jan Larsen, Mikkel N. Schmidt,
Michael Bache, Michael S. Schmidt,
Mogens H. Jakobsen, and Anja Boisen.
Improving the robustness of surface enhanced raman spectroscopy based sensors
by bayesian non-negative matrix factorization.
In Machine Learning for Signal Processing, IEEE International Workshop
on, (MLSP), 2014.
(pdf)
(doi:10.1109/MLSP.2014.6958925)
- [29]
- Karen Sandø Ambrosen,
Kristoffer Jon Albers, Tim Dyrby,
Mikkel N. Schmidt, and Morten Mørup.
Nonparametric bayesian clustering of structural whole brain connectivity in
full resolution.
In Pattern Recognition in NeuroImaging (PRNI), 2014.
(pdf)
(doi:10.1109/PRNI.2014.6858507)
- [30]
- Morten Mørup, Fumiko K.
Glückstad, Tue Herlau, and Mikkel N.
Schmidt.
Nonparametric statistical structuring of knowledge systems using binary feature
matches.
In Machine Learning for Signal Processing, IEEE International Workshop
on, (MLSP), 2014.
(pdf)
(doi:10.1109/MLSP.2014.6958905)
- [31]
- Mikkel N. Schmidt, Tue
Herlau, and Morten Mørup.
Discovering hierarchical structure in normal relational data.
In Cognitive Information Processing (CIP), 2014.
(pdf)
(doi:10.1109/CIP.2014.6844498)
- [32]
- Kristoffer Jon Albers, Andreas
Leon Aagaard Moth, Morten Mørup, and
Mikkel N. Schmidt.
Large scale inference in the infinite relational model: Gibbs sampling is not
enough.
In Machine Learning for Signal Processing, IEEE International Workshop on
(MLSP), 2013.
(pdf)
(doi:10.1109/MLSP.2013.6661904)
- [33]
- Karen Sandø Ambrosen, Tue
Herlau, Tim Dyrby, Mikkel N. Schmidt, and
Morten Mørup.
Comparing structural brain connectivity by the infinite relational model.
In Pattern Recognition in NeuroImaging (PRNI), pages 50–53, 2013.
(pdf)
(doi:10.1109/PRNI.2013.22)
- [34]
- Fumiko K. Glückstad, Tue
Herlau, Mikkel N. Schmidt, and Morten
Mørup.
Analysis of conceptualization patterns across groups of people.
In Technologies and Applications of Artificial Intelligence, Conference
on (TAAI), 2013.
(pdf)
(doi:10.1109/TAAI.2013.75)
- [35]
- Fumiko K. Glückstad, Tue
Herlau, Mikkel N. Schmidt, and Morten
Mørup.
Analysis of subjective conceptualizations towards collective conceptual
modelling.
In Japanese Society for Artificial Intelligence, Conference of the
(JSAI), 2013.
(pdf)
- [36]
- Fumiko K. Glückstad, Tue
Herlau, Mikkel N. Schmidt, and Morten
Mørup.
Unsupervised knowledge structuring: Application of infinite relational models
to the fca visualization.
In Signal Image Technology and Internet based Systems, International
Conference on (SITIS), pages 233–40, 2013.
(pdf)
(doi:10.1109/SITIS.2013.48)
- [37]
- Tue Herlau, Morten Mørup,
and Mikkel N. Schmidt.
Modeling temporal evolution and multiscale structure in networks'', machine
learning, international conference on (icml).
volume 28 of Proceedings of Machine Learning Research, pages
960–968, Atlanta, Georgia, USA, June 2013. PMLR.
(pdf)
- [38]
- Tommy S. Alstrøm,
Bjørn S. Jensen, Mikkel N. Schmidt,
Natalie V. Kostesha, and Jan Larsen.
Haussdorff and hellinger for colorimetric sensor array classification.
In Machine Learning for Signal Processing, IEEE International Workshop on
(MLSP), 2012.
(pdf)
(doi:10.1109/MLSP.2012.6349724)
- [39]
- Tue Herlau, Morten Mørup,
Mikkel N. Schmidt, and Lars Kai Hansen.
Detecting hierarchical structure in networks.
In Cognitive Information Processing (CIP), 2012.
(pdf)
(doi:10.1109/CIP.2012.6232913)
- [40]
- Tue Herlau, Morten Mørup,
Mikkel N. Schmidt, and Lars Kai Hansen.
Modeling dense relational data.
In Machine Learning for Signal Processing, IEEE International Workshop on
(MLSP), 2012.
(pdf)
(doi:10.1109/MLSP.2012.6349747)
- [41]
- Mikkel N. Schmidt, Stephen
Schwartz, and Jan Larsen.
Interactive 3d audio:
Enhancing awareness of details in immersive soundscapes?.
In 133rd Convention of the Audio Engineering Society, 2012.
(pdf)
- [42]
- Darko Zibar, Ole Winther,
Niccolo Franceschi, Robert Borkowski,
tonio Caballero, Mikkel N. Schmidt
Valeria Arlunno, Neil Guerrero Gonzales,
Bangning Mao, Knud J. Larsen, and
Idelfonso Tafur Monroy.
Nonlinear impairment compensation using expectation maximization for pdm 16-qam
systems.
In European Conference on Optical Communications (ECOC), 2012.
(doi:10.1364/OE.20.00B18)
- [43]
- Morten Mørup and Mikkel N.
Schmidt.
Transformation invariant sparse coding.
In Machine Learning for Signal Processing, IEEE International Workshop on
(MLSP), 2011.
(pdf)
(doi:10.1109/MLSP.2011.6064547)
- [44]
- Morten Mørup, Mikkel N.
Schmidt, and Lars Kai Hansen.
Infinite multiple membership relational modeling for complex networks.
In Machine Learning for Signal Processing, IEEE International Workshop on
(MLSP), 2011.
(pdf)
(doi:10.1109/MLSP.2011.6064546)
- [45]
- Mikkel N. Schmidt and Morten
Mørup.
Infinite non-negative matrix factorization.
In European Signal Processing Conference (EUSIPCO), 2010.
(pdf)
- [46]
- Morten Arngren, Mikkel N.
Schmidt, and Jan Larsen.
Bayesian nonnegative matrix factorization with volume prior for unmixing of
hyperspectral images.
In Machine Learning for Signal Processing, IEEE Workshop on
(MLSP), 2009.
(pdf)
(doi:10.1109/MLSP.2009.5306262)
- [47]
- Mikkel N. Schmidt.
Function factorization using warped gaussian processes.
In Machine Learning, International Conference on (ICML), 2009.
(pdf)
- [48]
- Mikkel N. Schmidt.
Linearly constrained matrix factorization for blind source separation.
In Advances in neural information processing (NIPS), 2009.
(pdf)
- [49]
- Mikkel N. Schmidt and Shakir
Mohamed.
Probabilistic non-negative tensor factorization using markov chain monte carlo.
In European Signal Processing Conference (EUSIPCO), 2009.
(pdf)
- [50]
- Mikkel N. Schmidt, Ole
Winther, and Lars Kai Hansen.
Bayesian non-negative matrix factorization.
In Independent Component Analysis and Signal Separation, International
Conference on (ICA), Springer Lecture Notes in Computer Science, Vol.
5441, pages 540–547, 2009.
(pdf)
(doi:10.1007/978-3-642-00599-2_68)
- [51]
- Hans Laurberg, Mikkel N.
Schmidt, Mads G. Christensen, and Søren H.
Jensen.
Structured non-negative matrix factorization with sparsity patterns.
In Signals, Systems and Computers, Asilomar Conference on, 2008.
(pdf)
(doi:10.1109/ACSSC.2008.5074714)
- [52]
- Mikkel N. Schmidt and Jan
Larsen.
Reduction of non-stationary noise using a non-negative latent variable
decomposition.
In Machine Learning for Signal Processing, IEEE Workshop on
(MLSP), pages 486–491, 2008.
(pdf)
(doi:10.1109/MLSP.2008.4685528)
- [53]
- Mikkel N. Schmidt and
Rasmus K. Olsson.
Linear regression on sparse features for single-channel speech separation.
In Applications of Signal Processing to Audio and Acoustics, IEEE
Workshop on (WASPAA), pages 26–29, 2007.
(pdf)
(doi:10.1109/ASPAA.2007.4393010)
- [54]
- Mikkel N. Schmidt, Jan
Larsen, and Fu-Tien Hsiao.
Wind noise reduction using non-negative sparse coding.
In Machine Learning for Signal Processing, IEEE International Workshop
on, (MLSP), pages 431–436, 2007.
(pdf)
(doi:10.1109/MLSP.2007.4414345)
- [55]
- Mikkel N. Schmidt and Morten
Mørup.
Non-negative matrix factor 2-d deconvolution for blind single channel source
separation.
In Independent Component Analysis, International Conference on (ICA),
Springer Lecture Notes in Computer Science, Vol.3889, pages 700–707,
2006.
(pdf)
(doi:10.1007/11679363_87)
- [56]
- Mikkel N. Schmidt and
Rasmus K. Olsson.
Single-channel speech separation using sparse non-negative matrix
factorization.
In International Conference on Spoken Language Processing,
(Interspeech), pages 1652–55, 2006.
(pdf)
Refereed abstracts and workshop contributions
- [1]
- Philip H. Jørgensen,
Søren F. V. Nielsen, Jesper L. Hinrich,
Mikkel N. Schmidt, Kristoffer H. Madsen, and
Morten Mørup.
Analysis of chromatographic data using the probabilistic parafac2.
In Machine Learning and the Physical Sciences, NeurIPS Workshop
on, 2019.
(pdf)
- [2]
- Fumiko K. Glückstad,
Mikkel N. Schmidt, and Morten Mørup.
Testing a model of destination image formation: Application of nonparametric
bayesian relational modeling to destination image analysis.
In Global Marketing Conference at Tokyo, pages 63–64, July 2018.
(pdf)
(doi:10.15444/GMC2018.01.07.02)
- [3]
- Kasper B. Frøhling,
Tommy S. Alstrøm, Michael Bache,
Michael S. Schmidt, Mikkel N. Schmidt,
Jan larsen, Mogens H. Jakobsen, and
Anja Boisen.
Statistical analysis of large areas of raman mapped dna functionalized gold
coated silicon nanopillar sers substrates.
In Advanced Vibrational Spectroscopym, International Conference on
(ICAVS), 2015.
(pdf)
- [4]
- Søren Føns Vind Nielsen,
Kristoffer Hougaard Madsen, Rasmus Røge,
Mikkel N. Schmidt, and Morten Mørup.
Nonparametric modeling of dynamic functional connectivity in fmri data.
In Machine Learning and Interpretation in Neuroimaging, NIPS Workshop on
(MLINI), 2015.
(pdf)
- [5]
- Kasper Winther Andersen,
Kristoffer H. Madsen, Hartwig Roman Siebner,
Mikkel N. Schmidt, Morten Mørup, and
Lars Kai Hansen.
Community structure in resting state complex networks.
In Human Brain Mapping, 2014.
(pdf)
- [6]
- Tue Herlau, Morten Mørup,
and Mikkel N. Schmidt.
Temporally evolving hierarchies in networks.
In NetSci, 2013.
(pdf)
- [7]
- Kasper Winther Andersen, Tue
Herlau, Morten Mørup, Mikkel N. Schmidt,
Mark Lyksborg Kristoffer H. Madsen, Tim Dyrby,
Hartwig Siebner, and Lars Kai Hansen.
Joint modelling of structural and functional brain networks.
In NIPS workshop on Machine Learning and Interpretation in
Neuroimaging, 2012.
(pdf)
- [8]
- Sune Lehmann, Morten
Mørup, and Mikkel N. Schmidt.
A bayesian generative model for pervasive overlap.
In NetSci, 2012.
(pdf)
- [9]
- Morten Mørup and Mikkel N.
Schmidt.
Efficient inference in the infinite multiple membership relational model.
In NIPS workshop on Bayesian nonparametric: Hope or hype, 2011.
(pdf)
- [10]
- Mikkel N. Schmidt, Morten
Mørup, and Tue Herlau.
Hierarchical models of complex networks.
In NIPS workshop on Bayesian nonparametric: Hope or hype, 2011.
(pdf)
- [11]
- Morten Mørup, Mikkel N.
Schmidt, and Lars Kai Hansen.
Infinite multiple membership relational modeling for complex networks.
In NIPS workshop on Networks across diciplines in theory and
applications, 2010.
(pdf)
- [12]
- Mikkel N. Schmidt and Morten
Mørup.
Reversible jump mcmc for bayesian nmf.
In NIPS workshop on Monte Carlo methods for Bayesian inference in modern
day applications, 2010.
(pdf)
Theses
- [1]
- Mikkel N. Schmidt.
Single-channel source separation using non-negative matrix
factorization.
PhD thesis, Technical University of Denmark, 2008.
(pdf)
- [2]
- Mikkel N. Schmidt and Jens
Seiersen.
Perceptual unitary esprit algorithm.
Master's thesis, Aalborg University, 2003.
(pdf)
Other
- [1]
- Morten Mørup and
Mikkel N. Schmidt.
Sparse non-negative matrix factor 2-d deconvolution.
(pdf)
- [2]
- Morten Mørup and
Mikkel N. Schmidt.
Sparse non-negative tensor 2d deconvolution (sntf2d) for multi channel
time-frequency analysis.
(pdf)
- [3]
- Mikkel N.
Schmidt.
Speech separation using non-negative features and sparse non-negative matrix
factorization 2007.
(pdf)
- [4]
- Mikkel N. Schmidt and
Morten Mørup.
Sparse non-negative matrix factor 2-d deconvolution for automatic transcription
of polyphonic music.
(pdf)
- [5]
- Mikkel N. Schmidt and
Rasmus K. Olsson.
Feature space reconstruction for single-channel speech separation.
(pdf)
- [1]
- Kristoffer J. Albers, Morten
Mørup, and Mikkel N. Schmidt.
Local modes in the posterior distribution of Dirichlet process mixture
models.
2017.
- [2]
- Kristoffer Jon Albers,
Karen Sandø Ambrossen, Rasmus Røge,
Matthew G. Liptrot, Tue Herlau,
Kasper Winther Andersen, Hartwig R. Siebner,
Lars Kai Hansen, Tim B. Dyrby,
Kristoffer H. Madsen, Mikkel N. Schmidt, and
Morten Mørup.
Functional whole-brain parcellation improved by the inclusion of structural
connectivity.
2017.
- [3]
- Kristoffer Jon Albers, Morten
Mørup, Mikkel N. Schmidt, and Fumiko K.
Glückstad.
Predictive evaluation of human value segmentations.
2017.
- [4]
- Tue
Herlau, Morten Mørup, Yee Whye Teh, and
Mikkel N. Schmidt.
Adaptive reconfiguration moves for
efficient Markov chain sampling.
2014.
- [5]
- Tue Herlau, Mikkel N.
Schmidt, and Morten Mørup.
Bayesian dropout.
2013.
- [6]
- Mikkel N. Schmidt, Morten
Mørup, and Tue Herlau.
Nonparametric bayesian models of
hierarchical structure in complex networks.
2012.