Mikkel N. Schmidt

According to our database, Mikkel N. Schmidt authored at least 67 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Probabilistic PARAFAC2.

Philip J. H. Jørgensen, Søren Føns Vind Nielsen, Jesper Løve Hinrich, Mikkel N. Schmidt, Kristoffer H. Madsen, Morten Mørup

Entropy, August, 2024

Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity.

Bo Li, Yasin Esfandiari, Mikkel N. Schmidt, Tommy Sonne Alstrøm, Sebastian U. Stich

Trans. Mach. Learn. Res., 2024

FLEXtime: Filterbank learning for explaining time series.

Thea Brüsch, Kristoffer K. Wickstrøm, Mikkel N. Schmidt, Robert Jenssen, Tommy S. Alstrøm

CoRR, 2024

Contrastive random lead coding for channel-agnostic self-supervision of biosignals.

Thea Brüsch, Mikkel N. Schmidt, Tommy S. Alstrøm

CoRR, 2024

Explaining time series models using frequency masking.

Thea Brüsch, Kristoffer K. Wickstrøm, Mikkel N. Schmidt, Tommy S. Alstrøm, Robert Jenssen

CoRR, 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

Proceedings of the 34th IEEE International Workshop on Machine Learning for Signal Processing, 2024

An improved analysis of per-sample and per-update clipping in federated learning.

Bo Li, Xiaowen Jiang, Mikkel N. Schmidt, Tommy Sonne Alstrøm, Sebastian U. Stich

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Coherent energy and force uncertainty in deep learning force fields.

Peter Bjørn Jørgensen, Jonas Busk, Ole Winther, Mikkel N. Schmidt

CoRR, 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

Amortized Variational Peak Fitting For Spectroscopic Data.

David Frich Hansen, Tommy Sonne Alstrøm, Mikkel N. Schmidt

Proceedings of the 33rd IEEE International Workshop on Machine Learning for Signal Processing, 2023

Multi-View Self-Supervised Learning For Multivariate Variable-Channel Time Series.

Thea Brüsch, Mikkel N. Schmidt, Tommy S. Alstrøm

Proceedings of the 33rd IEEE International Workshop on Machine Learning for Signal Processing, 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

Proceedings of the IEEE International Conference on Acoustics, 2023

On the Effectiveness of Partial Variance Reduction in Federated Learning with Heterogeneous Data.

Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

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

Partial Variance Reduction improves Non-Convex Federated learning on heterogeneous data.

Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich

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

Raman Spectrum Matching with Contrastive Representation Learning.

Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm

CoRR, 2022

Bayesian dropout.

Tue Herlau, Mikkel N. Schmidt, Morten Mørup

Proceedings of the 13th International Conference on Ambient Systems, 2022

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

Matrix Product States for Inference in Discrete Probabilistic Models.

Rasmus Bonnevie, Mikkel N. Schmidt

J. Mach. Learn. Res., 2021

Programmatic Policy Extraction by Iterative Local Search.

Rasmus Larsen, Mikkel Nørgaard Schmidt

Proceedings of the Inductive Logic Programming - 30th International Conference, 2021

2020

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

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

Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, 2019

Peak Detection and Baseline Correction Using a Convolutional Neural Network.

Mikkel N. Schmidt, Tommy S. Alstrøm, Marcus Svendstorp, Jan Larsen

Proceedings of the IEEE International Conference on Acoustics, 2019

2018

Predictive assessment of models for dynamic functional connectivity.

Søren Føns Vind Nielsen, Mikkel N. Schmidt, Kristoffer Henriksen, Morten Mørup

NeuroImage, 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

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

Proceedings of the 2018 International Workshop on Pattern Recognition in Neuroimaging, 2018

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

Proceedings of the 2018 IEEE 42nd Annual Computer Software and Applications Conference, 2018

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

Modeling dynamic functional connectivity using a wishart mixture model.

Søren Føns Vind Nielsen, Kristoffer Hougaard Madsen, Mikkel N. Schmidt, Morten Mørup

Proceedings of the 2017 International Workshop on Pattern Recognition in Neuroimaging, 2017

Difference-of-Convex optimization for variational kl-corrected inference in dirichlet process mixtures.

Rasmus Bonnevie, Mikkel N. Schmidt, Morten Mørup

Proceedings of the 27th IEEE International Workshop on Machine Learning for Signal Processing, 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

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

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

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

2016

Completely random measures for modelling block-structured sparse networks.

Tue Herlau, Mikkel N. Schmidt, Morten Mørup

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Bayesian latent feature modeling for modeling bipartite networks with overlapping groups.

Philip H. Jørgensen, Morten Mørup, Mikkel N. Schmidt, Tue Herlau

Proceedings of the 26th IEEE International Workshop on Machine Learning for Signal Processing, 2016

The influence of hyper-parameters in the infinite relational model.

Kristoffer Jon Albers, Morten Mørup, Mikkel N. Schmidt

Proceedings of the 26th IEEE International Workshop on Machine Learning for Signal Processing, 2016

2015

Unsupervised segmentation of task activated regions in fMRI.

Rasmus Erbou Røge, Kristoffer Hougaard Madsen, Mikkel N. Schmidt, Morten Mørup

Proceedings of the 25th IEEE International Workshop on Machine Learning for Signal Processing, 2015

Numerical approximations for speeding up MCMC inference in the infinite relational model.

Mikkel N. Schmidt, Kristoffer Jon Albers

Proceedings of the 23rd European Signal Processing Conference, 2015

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

Errata to "Bayesian Community Detection" (<i>Neural Computation</i>, Sept. 2012 , Vol. 24, No. 9: 2434-2456).

Morten Mørup, Mikkel N. Schmidt

Neural Comput., 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

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

Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2014

Nonparametric statistical structuring of knowledge systems using binary feature matches.

Morten Mørup, Fumiko Kano Glückstad, Tue Herlau, Mikkel N. Schmidt

Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 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

Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2014

Discovering hierarchical structure in normal relational data.

Mikkel N. Schmidt, Tue Herlau, Morten Mørup

Proceedings of the 4th International Workshop on Cognitive Information Processing, 2014

2013

Nonparametric Bayesian Modeling of Complex Networks: An Introduction.

Mikkel N. Schmidt, Morten Mørup

IEEE Signal Process. Mag., 2013

Analysis of Conceptualization Patterns across Groups of People.

Fumiko Kano Glückstad, Tue Herlau, Mikkel N. Schmidt, Morten Mørup, Rafal Rzepka, Kenji Araki

Proceedings of the Conference on Technologies and Applications of Artificial Intelligence, 2013

Unsupervised Knowledge Structuring: Application of Infinite Relational Models to the FCA Visualization.

Fumiko Kano Glückstad, Tue Herlau, Mikkel N. Schmidt, Morten Mørup

Proceedings of the Ninth International Conference on Signal-Image Technology & Internet-Based Systems, 2013

Comparing Structural Brain Connectivity by the Infinite Relational Model.

Karen Sandø Ambrosen, Tue Herlau, Tim B. Dyrby, Mikkel N. Schmidt, Morten Mørup

Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 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

Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2013

Modeling Temporal Evolution and Multiscale Structure in Networks.

Tue Herlau, Morten Mørup, Mikkel N. Schmidt

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Bayesian Community Detection.

Morten Mørup, Mikkel N. Schmidt

Neural Comput., 2012

Modelling dense relational data.

Tue Herlau, Morten Mørup, Mikkel N. Schmidt, Lars Kai Hansen

Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2012

Haussdorff and hellinger for colorimetric sensor array classification.

Tommy S. Alstrøm, Bjørn Sand Jensen, Mikkel N. Schmidt, Natalie V. Kostesha, Jan Larsen

Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2012

Detecting hierarchical structure in networks.

Tue Herlau, Morten Mørup, Mikkel N. Schmidt, Lars Kai Hansen

Proceedings of the 3rd International Workshop on Cognitive Information Processing, 2012

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

Infinite multiple membership relational modeling for complex networks.

Morten Mørup, Mikkel N. Schmidt, Lars Kai Hansen

Proceedings of the 2011 IEEE International Workshop on Machine Learning for Signal Processing, 2011

Transformation invariant sparse coding.

Morten Mørup, Mikkel N. Schmidt

Proceedings of the 2011 IEEE International Workshop on Machine Learning for Signal Processing, 2011

2010

Infinite non-negative matrix factorization.

Mikkel N. Schmidt, Morten Mørup

Proceedings of the 18th European Signal Processing Conference, 2010

2009

Linearly constrained Bayesian matrix factorization for blind source separation.

Mikkel N. Schmidt

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Bayesian Non-negative Matrix Factorization.

Mikkel N. Schmidt, Ole Winther, Lars Kai Hansen

Proceedings of the Independent Component Analysis and Signal Separation, 2009

Function factorization using warped Gaussian processes.

Mikkel N. Schmidt

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Probabilistic non-negative tensor factorization using Markov chain Monte Carlo.

Mikkel N. Schmidt, Shakir Mohamed

Proceedings of the 17th European Signal Processing Conference, 2009

2008

Nonnegative Matrix Factorization with Gaussian Process Priors.

Mikkel N. Schmidt, Hans Laurberg

Comput. Intell. Neurosci., 2008

Structured non-negative matrix factorization with sparsity patterns.

Hans Laurberg, Mikkel N. Schmidt, Mads Græsbøll Christensen, Søren Holdt Jensen

Proceedings of the 42nd Asilomar Conference on Signals, Systems and Computers, 2008

2006

Single-channel speech separation using sparse non-negative matrix factorization.

Mikkel N. Schmidt, Rasmus Kongsgaard Olsson

Proceedings of the Ninth International Conference on Spoken Language Processing, 2006

Nonnegative Matrix Factor 2-D Deconvolution for Blind Single Channel Source Separation.

Mikkel N. Schmidt, Morten Mørup

Proceedings of the Independent Component Analysis and Blind Signal Separation, 2006