Nonnegative Matrix Factor 2-D Deconvolution for Blind Single Channel Source Separation. - Mikkel N. Schmidt; Morten Mørup (ICA, 2006)

Single-channel speech separation using sparse non-negative matrix factorization. - Mikkel N. Schmidt; Rasmus Kongsgaard Olsson (INTERSPEECH, 2006)

Structured non-negative matrix factorization with sparsity patterns. - Hans Laurberg; Mikkel N. Schmidt; Mads Græsbøll Christensen; Søren Holdt Jensen (ACSCC, 2008)

Nonnegative Matrix Factorization with Gaussian Process Priors. - Mikkel N. Schmidt; Hans Laurberg (Comput. Intell. Neurosci., 2008)

Probabilistic non-negative tensor factorization using Markov chain Monte Carlo. - Mikkel N. Schmidt; Shakir Mohamed (EUSIPCO, 2009)

Function factorization using warped Gaussian processes. - Mikkel N. Schmidt (ICML, 2009)

Bayesian Non-negative Matrix Factorization. - Mikkel N. Schmidt; Ole Winther; Lars Kai Hansen (ICA, 2009)

Linearly constrained Bayesian matrix factorization for blind source separation. - Mikkel N. Schmidt (NIPS, 2009)

Infinite non-negative matrix factorization. - Mikkel N. Schmidt; Morten Mørup (EUSIPCO, 2010)

Transformation invariant sparse coding. - Morten Mørup; Mikkel N. Schmidt (MLSP, 2011)

Infinite multiple membership relational modeling for complex networks. - Morten Mørup; Mikkel N. Schmidt; Lars Kai Hansen (MLSP, 2011)

Unmixing of Hyperspectral Images using Bayesian Non-negative Matrix Factorization with Volume Prior. - Morten Arngren; Mikkel N. Schmidt; Jan Larsen (J. Signal Process. Syst., 2011)

Detecting hierarchical structure in networks. - Tue Herlau; Morten Mørup; Mikkel N. Schmidt; Lars Kai Hansen (CIP, 2012)

Haussdorff and hellinger for colorimetric sensor array classification. - Tommy S. Alstrøm; Bjørn Sand Jensen; Mikkel N. Schmidt; Natalie V. Kostesha; Jan Larsen (MLSP, 2012)

Modelling dense relational data. - Tue Herlau; Morten Mørup; Mikkel N. Schmidt; Lars Kai Hansen (MLSP, 2012)

Bayesian Community Detection. - Morten Mørup; Mikkel N. Schmidt (Neural Comput., 2012)

Modeling Temporal Evolution and Multiscale Structure in Networks. - Tue Herlau; Morten Mørup; Mikkel N. Schmidt (ICML (3), 2013)

Large scale inference in the Infinite Relational Model: Gibbs sampling is not enough. - Kristoffer Jon Albers; Andreas Leon Aagaard Moth; Morten Mørup; Mikkel N. Schmidt (MLSP, 2013)

Comparing Structural Brain Connectivity by the Infinite Relational Model. - Karen Sandø Ambrosen; Tue Herlau; Tim B. Dyrby; Mikkel N. Schmidt; Morten Mørup (PRNI, 2013)

Unsupervised Knowledge Structuring: Application of Infinite Relational Models to the FCA Visualization. - Fumiko Kano Glückstad; Tue Herlau; Mikkel N. Schmidt; Morten Mørup (SITIS, 2013)

Analysis of Conceptualization Patterns across Groups of People. - Fumiko Kano Glückstad; Tue Herlau; Mikkel N. Schmidt; Morten Mørup; Rafal Rzepka; Kenji Araki (TAAI, 2013)

Nonparametric Bayesian Modeling of Complex Networks: An Introduction. - Mikkel N. Schmidt; Morten Mørup (IEEE Signal Process. Mag., 2013)

Discovering hierarchical structure in normal relational data. - Mikkel N. Schmidt; Tue Herlau; Morten Mørup (CIP, 2014)

Improving the robustness of Surface Enhanced Raman Spectroscopy based sensors by Bayesian Non-negative Matrix Factorization. - Tommy S. Alstrøm; Kasper B. Frohling; Jan Larsen; Mikkel N. Schmidt; Michael Bache; Michael S. Schmidt; Mogens H. Jakobsen; Anja Boisen (MLSP, 2014)

Nonparametric statistical structuring of knowledge systems using binary feature matches. - Morten Mørup; Fumiko Kano Glückstad; Tue Herlau; Mikkel N. Schmidt (MLSP, 2014)

Nonparametric Bayesian clustering of structural whole brain connectivity in full image resolution. - Karen Sandø Ambrosen; Kristoffer Jon Albers; Tim B. Dyrby; Mikkel N. Schmidt; Morten Mørup (PRNI, 2014)

Cross-categorization of legal concepts across boundaries of legal systems: in consideration of inferential links. - Fumiko Kano Glückstad; Tue Herlau; Mikkel N. Schmidt; Morten Mørup (Artif. Intell. Law, 2014)

Errata to "Bayesian Community Detection" (Neural Computation, Sept. 2012 , Vol. 24, No. 9: 2434-2456). - Morten Mørup; Mikkel N. Schmidt (Neural Comput., 2014)

Non-parametric Bayesian graph models reveal community structure in resting state fMRI. - Kasper Winther Andersen; Kristoffer Hougaard Madsen; Hartwig Roman Siebner; Mikkel N. Schmidt; Morten Mørup; Lars Kai Hansen (NeuroImage, 2014)

Numerical approximations for speeding up MCMC inference in the infinite relational model. - Mikkel N. Schmidt; Kristoffer Jon Albers (EUSIPCO, 2015)

Unsupervised segmentation of task activated regions in fMRI. - Rasmus Erbou Røge; Kristoffer Hougaard Madsen; Mikkel N. Schmidt; Morten Mørup (MLSP, 2015)

The influence of hyper-parameters in the infinite relational model. - Kristoffer Jon Albers; Morten Mørup; Mikkel N. Schmidt (MLSP, 2016)

Bayesian latent feature modeling for modeling bipartite networks with overlapping groups. - Philip H. Jørgensen; Morten Mørup; Mikkel N. Schmidt; Tue Herlau (MLSP, 2016)

Completely random measures for modelling block-structured sparse networks. - Tue Herlau; Mikkel N. Schmidt; Morten Mørup (NIPS, 2016)

A pseudo-Voigt component model for high-resolution recovery of constituent spectra in Raman spectroscopy. - Tommy S. Alstrøm; Mikkel N. Schmidt; Tomas Rindzevicius; Anja Boisen; Jan Larsen (ICASSP, 2017)

Scalable group level probabilistic sparse factor analysis. - Jesper Løve Hinrich; Søren Føns Vind Nielsen; Nicolai André Brogaard Riis; Casper T. Eriksen; Jacob Frosig; Marco D. F. Kristensen; Mikkel N. Schmidt; Kristoffer Hougaard Madsen; Morten Mørup (ICASSP, 2017)

Difference-of-Convex optimization for variational kl-corrected inference in dirichlet process mixtures. - Rasmus Bonnevie; Mikkel N. Schmidt; Morten Mørup (MLSP, 2017)

Modeling dynamic functional connectivity using a wishart mixture model. - Søren Føns Vind Nielsen; Kristoffer Hougaard Madsen; Mikkel N. Schmidt; Morten Mørup (PRNI, 2017)

Infinite von Mises-Fisher Mixture Modeling of Whole Brain fMRI Data. - Rasmus Erbou Røge; Kristoffer Hougaard Madsen; Mikkel N. Schmidt; Morten Mørup (Neural Comput., 2017)

Understanding Mindsets Across Markets, Internationally: A Public-Private Innovation Project for Developing a Tourist Data Analytic Platform. - Kristoffer Jon Albers; Mikkel N. Schmidt; Morten Mørup; Marisciel Litong-Palima; Rasmus Bonnevie; Fumiko Kano Glückstad (COMPSAC (2), 2018)

Testing group differences in state transition structure of dynamic functional connectivity models. - Søren Føns Vind Nielsen; Diego Vidaurre; Kristoffer Hougaard Madsen; Mikkel N. Schmidt; Morten Mørup (PRNI, 2018)

Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials. - Peter Bjørn Jørgensen; Karsten Wedel Jacobsen; Mikkel N. Schmidt (CoRR, 2018)

Predictive assessment of models for dynamic functional connectivity. - Søren Føns Vind Nielsen; Mikkel N. Schmidt; Kristoffer Henriksen; Morten Mørup (NeuroImage, 2018)

Peak Detection and Baseline Correction Using a Convolutional Neural Network. - Mikkel N. Schmidt; Tommy S. Alstrøm; Marcus Svendstorp; Jan Larsen (ICASSP, 2019)

A Bayesian Generative Model With Gaussian Process Priors For Thermomechanical Analysis Of Micro-Resonators. - Maximillian Fornitz Vording; Peter O. Okeyo; Juan J. R. Guillamón; Peter E. Larsen; Mikkel N. Schmidt; Tommy S. Alstrøm (MLSP, 2019)

Validation of structural brain connectivity networks: The impact of scanning parameters. - Karen Sandø Ambrosen; Simon F. Eskildsen; Max Hinne; Kristine Krug; Henrik Lundell; Mikkel N. Schmidt; Marcel A. J. van Gerven; Morten Mørup; Tim B. Dyrby (NeuroImage, 2020)

Programmatic Policy Extraction by Iterative Local Search. - Rasmus Larsen 001; Mikkel Nørgaard Schmidt (ILP, 2021)

Matrix Product States for Inference in Discrete Probabilistic Models. - Rasmus Bonnevie; Mikkel N. Schmidt (J. Mach. Learn. Res., 2021)

Using connectomics for predictive assessment of brain parcellations. - Kristoffer Jon Albers; Karen Sandø Ambrosen; Matthew G. Liptrot; Tim B. Dyrby; Mikkel N. Schmidt; Morten Mørup (NeuroImage, 2021)

Bayesian dropout. - Tue Herlau; Mikkel N. Schmidt; Morten Mørup (ANT/EDI40, 2022)

Raman Spectrum Matching with Contrastive Representation Learning. - Bo Li 119; Mikkel N. Schmidt; Tommy S. Alstrøm (CoRR, 2022)

End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities. - Muralikrishnan Srinivasan; Jinxiang Song; Alexander Grabowski; Krzysztof Szczerba; Holger K. Iversen; Mikkel N. Schmidt; Darko Zibar; Jochen Schröder; Anders Larsson; Christian Häger; Henk Wymeersch (CoRR, 2022)

Partial Variance Reduction improves Non-Convex Federated learning on heterogeneous data. - Bo Li 119; Mikkel N. Schmidt; Tommy S. Alstrøm; Sebastian U. Stich (CoRR, 2022)

Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks. - Jonas Busk; Peter Bjørn Jørgensen; Arghya Bhowmik; Mikkel N. Schmidt; Ole Winther; Tejs Vegge (Mach. Learn. Sci. Technol., 2022)

On the Effectiveness of Partial Variance Reduction in Federated Learning with Heterogeneous Data. - Bo Li 119; Mikkel N. Schmidt; Tommy S. Alstrøm; Sebastian U. Stich (CVPR, 2023)

Angular Central Gaussian and Watson Mixture Models for Assessing Dynamic Functional Brain Connectivity During a Motor Task. - Anders S. Olsen; Emil Ortvald; Kristoffer H. Madsen; Mikkel N. Schmidt; Morten Mørup (ICASSP Workshops, 2023)

Multi-View Self-Supervised Learning For Multivariate Variable-Channel Time Series. - Thea Brüsch; Mikkel N. Schmidt; Tommy S. Alstrøm (MLSP, 2023)

Amortized Variational Peak Fitting For Spectroscopic Data. - David Frich Hansen; Tommy Sonne Alstrøm; Mikkel N. Schmidt (MLSP, 2023)

Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces. - Jonas Busk; Mikkel N. Schmidt; Ole Winther; Tejs Vegge; Peter Bjørn Jørgensen (CoRR, 2023)

Coherent energy and force uncertainty in deep learning force fields. - Peter Bjørn Jørgensen; Jonas Busk; Ole Winther; Mikkel N. Schmidt (CoRR, 2023)

An improved analysis of per-sample and per-update clipping in federated learning. - Bo Li 119; Xiaowen Jiang; Mikkel N. Schmidt; Tommy Sonne Alstrøm; Sebastian U. Stich (ICLR, 2024)

Modeling Human Responses by Ordinal Archetypal Analysis. - Anna Emilie J. Wedenborg; Michael Alexander Harborg; Andreas Bigom; Oliver Elmgreen; Marcus Presutti; Andreas Råskov; Fumiko Kano Glückstad; Mikkel N. Schmidt; Morten Mørup (MLSP, 2024)

Equivariant Neural Diffusion for Molecule Generation. - François Cornet; Grigory Bartosh; Mikkel N. Schmidt; Christian Andersson Naesseth (NeurIPS, 2024)

Explaining time series models using frequency masking. - Thea Brüsch; Kristoffer K. Wickstrøm; Mikkel N. Schmidt; Tommy S. Alstrøm; Robert Jenssen (CoRR, 2024)

Contrastive random lead coding for channel-agnostic self-supervision of biosignals. - Thea Brüsch; Mikkel N. Schmidt; Tommy S. Alstrøm (CoRR, 2024)

FLEXtime: Filterbank learning for explaining time series. - Thea Brüsch; Kristoffer K. Wickstrøm; Mikkel N. Schmidt; Robert Jenssen; Tommy S. Alstrøm (CoRR, 2024)

Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity. - Bo Li 119; Yasin Esfandiari; Mikkel N. Schmidt; Tommy Sonne Alstrøm; Sebastian U. Stich (Trans. Mach. Learn. Res., 2024)

Probabilistic PARAFAC2. - Philip J. H. Jørgensen; Søren Føns Vind Nielsen; Jesper Løve Hinrich; Mikkel N. Schmidt; Kristoffer H. Madsen; Morten Mørup (Entropy, 2024)

On Local Posterior Structure in Deep Ensembles. - Mikkel Jordahn; Jonas Vestergaard Jensen; Mikkel N. Schmidt; Michael Riis Andersen (AISTATS, 2025)

FreqRISE: Explaining time series using frequency masking. - Thea Brüsch; Kristoffer Knutsen Wickstrøm; Mikkel N. Schmidt; Tommy Sonne Alstrøm; Robert Jenssen (NLDL, 2025)

Mode Mismatch Mitigation in Gaussian-Modulated CV-QKD. - Svitlana Matsenko; Amirhossein Ghazisaeidi; Marcin Jarzyna; Mikkel N. Schmidt; Søren F. Nielsen; Konrad Banaszek; Darko Zibar (CoRR, 2025)

Kinetic Langevin Diffusion for Crystalline Materials Generation. - François Cornet; Federico Bergamin; Arghya Bhowmik; Juan Maria Garcia Lastra; Jes Frellsen; Mikkel N. Schmidt (CoRR, 2025)