Mikkel N. Schmidt

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 37

Top Topics

Nonparametric Statistical

Message Passing

Functional Connectivity

Top Venues

Publications

Bo Li, Xiaowen Jiang, Mikkel N. Schmidt, Tommy Sonne Alstrøm, Sebastian U. Stich
An improved analysis of per-sample and per-update clipping in federated learning. ICLR (2024)
Bo Li, Yasin Esfandiari, Mikkel N. Schmidt, Tommy Sonne Alstrøm, Sebastian U. Stich
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity. Trans. Mach. Learn. Res. 2024 (2024)
Thea Brüsch, Kristoffer K. Wickstrøm, Mikkel N. Schmidt, Tommy S. Alstrøm, Robert Jenssen
Explaining time series models using frequency masking. CoRR (2024)
Anders S. Olsen, Emil Ortvald, Kristoffer H. Madsen, Mikkel N. Schmidt, Morten Mørup
Angular Central Gaussian and Watson Mixture Models for Assessing Dynamic Functional Brain Connectivity During a Motor Task. ICASSP Workshops (2023)
Bo Li, Yasin Esfandiari, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity. CoRR (2023)
Thea Brüsch, Mikkel N. Schmidt, Tommy S. Alstrøm
Multi-View Self-Supervised Learning For Multivariate Variable-Channel Time Series. MLSP (2023)
Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich
On the Effectiveness of Partial Variance Reduction in Federated Learning with Heterogeneous Data. CVPR (2023)
David Frich Hansen, Tommy Sonne Alstrøm, Mikkel N. Schmidt
Amortized Variational Peak Fitting For Spectroscopic Data. MLSP (2023)
Peter Bjørn Jørgensen, Jonas Busk, Ole Winther, Mikkel N. Schmidt
Coherent energy and force uncertainty in deep learning force fields. CoRR (2023)
Jonas Busk, Mikkel N. Schmidt, Ole Winther, Tejs Vegge, Peter Bjørn Jørgensen
Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces. CoRR (2023)
Thea Brüsch, Mikkel N. Schmidt, Tommy S. Alstrøm
Multi-view self-supervised learning for multivariate variable-channel time series. CoRR (2023)
Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm
Raman Spectrum Matching with Contrastive Representation Learning. CoRR (2022)
Tue Herlau, Mikkel N. Schmidt, Morten Mørup
Bayesian dropout. ANT/EDI40 (2022)
Jonas Busk, Peter Bjørn Jørgensen, Arghya Bhowmik, Mikkel N. Schmidt, Ole Winther, Tejs Vegge
Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks. Mach. Learn. Sci. Technol. 3 (1) (2022)
Muralikrishnan Srinivasan, Jinxiang Song, Alexander Grabowski, Krzysztof Szczerba, Holger K. Iversen, Mikkel N. Schmidt, Darko Zibar, Jochen Schröder, Anders Larsson, Christian Häger, Henk Wymeersch
End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities. CoRR (2022)
Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich
Partial Variance Reduction improves Non-Convex Federated learning on heterogeneous data. CoRR (2022)
Jonas Busk, Peter Bjørn Jørgensen, Arghya Bhowmik, Mikkel N. Schmidt, Ole Winther, Tejs Vegge
Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks. CoRR (2021)
Kristoffer Jon Albers, Karen Sando Ambrosen, Matthew G. Liptrot, Tim B. Dyrby, Mikkel N. Schmidt, Morten Mørup
Using connectomics for predictive assessment of brain parcellations. NeuroImage 238 (2021)
Rasmus Bonnevie, Mikkel N. Schmidt
Matrix Product States for Inference in Discrete Probabilistic Models. J. Mach. Learn. Res. 22 (2021)
Karen Sando Ambrosen, Simon F. Eskildsen, Max Hinne, Kristine Krug, Henrik Lundell, Mikkel N. Schmidt, Marcel A. J. van Gerven, Morten Mørup, Tim B. Dyrby
Validation of structural brain connectivity networks: The impact of scanning parameters. NeuroImage 204 (2020)
Mikkel N. Schmidt, Tommy S. Alstrøm, Marcus Svendstorp, Jan Larsen
Peak Detection and Baseline Correction Using a Convolutional Neural Network. ICASSP (2019)
Maximillian Fornitz Vording, Peter O. Okeyo, Juan J. R. Guillamón, Peter E. Larsen, Mikkel N. Schmidt, Tommy S. Alstrøm
A Bayesian Generative Model With Gaussian Process Priors For Thermomechanical Analysis Of Micro-Resonators. MLSP (2019)
Peter Bjørn Jørgensen, Karsten Wedel Jacobsen, Mikkel N. Schmidt
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials. CoRR (2018)
Søren Føns Vind Nielsen, Diego Vidaurre, Kristoffer Hougaard Madsen, Mikkel N. Schmidt, Morten Mørup
Testing group differences in state transition structure of dynamic functional connectivity models. PRNI (2018)
Søren Føns Vind Nielsen, Mikkel N. Schmidt, Kristoffer Henriksen, Morten Mørup
Predictive assessment of models for dynamic functional connectivity. NeuroImage 171 (2018)
Kristoffer Jon Albers, Mikkel N. Schmidt, Morten Mørup, Marisciel Litong-Palima, Rasmus Bonnevie, Fumiko Kano Glückstad
Understanding Mindsets Across Markets, Internationally: A Public-Private Innovation Project for Developing a Tourist Data Analytic Platform. COMPSAC (2) (2018)
Philip J. H. Jørgensen, Søren Føns Vind Nielsen, Jesper Løve Hinrich, Mikkel N. Schmidt, Kristoffer Hougaard Madsen, Morten Mørup
Probabilistic PARAFAC2. CoRR (2018)
Søren Føns Vind Nielsen, Kristoffer Hougaard Madsen, Mikkel N. Schmidt, Morten Mørup
Modeling dynamic functional connectivity using a wishart mixture model. PRNI (2017)
Rasmus Bonnevie, Mikkel N. Schmidt, Morten Mørup
Difference-of-Convex optimization for variational kl-corrected inference in dirichlet process mixtures. MLSP (2017)
Rasmus Erbou Roge, Kristoffer Hougaard Madsen, Mikkel N. Schmidt, Morten Mørup
Infinite von Mises-Fisher Mixture Modeling of Whole Brain fMRI Data. Neural Comput. 29 (10) (2017)
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
Scalable group level probabilistic sparse factor analysis. ICASSP (2017)
Tommy S. Alstrøm, Mikkel N. Schmidt, Tomas Rindzevicius, Anja Boisen, Jan Larsen
A pseudo-Voigt component model for high-resolution recovery of constituent spectra in Raman spectroscopy. ICASSP (2017)
Tue Herlau, Mikkel N. Schmidt, Morten Mørup
Completely random measures for modelling block-structured sparse networks. NIPS (2016)
Kristoffer Jon Albers, Morten Mørup, Mikkel N. Schmidt
The influence of hyper-parameters in the infinite relational model. MLSP (2016)
Philip H. Jørgensen, Morten Mørup, Mikkel N. Schmidt, Tue Herlau
Bayesian latent feature modeling for modeling bipartite networks with overlapping groups. MLSP (2016)
Mikkel N. Schmidt, Kristoffer Jon Albers
Numerical approximations for speeding up MCMC inference in the infinite relational model. EUSIPCO (2015)
Rasmus Erbou Roge, Kristoffer Hougaard Madsen, Mikkel N. Schmidt, Morten Mørup
Unsupervised segmentation of task activated regions in fMRI. MLSP (2015)
Mikkel N. Schmidt, Tue Herlau, Morten Mørup
Discovering hierarchical structure in normal relational data. CIP (2014)
Karen Sando Ambrosen, Kristoffer Jon Albers, Tim B. Dyrby, Mikkel N. Schmidt, Morten Mørup
Nonparametric Bayesian clustering of structural whole brain connectivity in full image resolution. PRNI (2014)
Morten Mørup, Mikkel N. Schmidt
, Sept. 2012 , Vol. 24, No. 9: 2434-2456). Neural Comput. 26 (6) (2014)
Tommy S. Alstrøm, Kasper B. Frohling, Jan Larsen, Mikkel N. Schmidt, Michael Bache, Michael S. Schmidt, Mogens H. Jakobsen, Anja Boisen
Improving the robustness of Surface Enhanced Raman Spectroscopy based sensors by Bayesian Non-negative Matrix Factorization. MLSP (2014)
Morten Mørup, Fumiko Kano Glückstad, Tue Herlau, Mikkel N. Schmidt
Nonparametric statistical structuring of knowledge systems using binary feature matches. MLSP (2014)
Kasper Winther Andersen, Kristoffer Hougaard Madsen, Hartwig Roman Siebner, Mikkel N. Schmidt, Morten Mørup, Lars Kai Hansen
Non-parametric Bayesian graph models reveal community structure in resting state fMRI. NeuroImage 100 (2014)
Fumiko Kano Glückstad, Tue Herlau, Mikkel N. Schmidt, Morten Mørup
Cross-categorization of legal concepts across boundaries of legal systems: in consideration of inferential links. Artif. Intell. Law 22 (1) (2014)
Mikkel N. Schmidt, Morten Mørup
Nonparametric Bayesian Modeling of Complex Networks: An Introduction. IEEE Signal Process. Mag. 30 (3) (2013)
Tue Herlau, Morten Mørup, Mikkel N. Schmidt
Modeling Temporal Evolution and Multiscale Structure in Networks. ICML (3) (2013)
Karen Sando Ambrosen, Tue Herlau, Tim B. Dyrby, Mikkel N. Schmidt, Morten Mørup
Comparing Structural Brain Connectivity by the Infinite Relational Model. PRNI (2013)
Fumiko Kano Glückstad, Tue Herlau, Mikkel N. Schmidt, Morten Mørup
Unsupervised Knowledge Structuring: Application of Infinite Relational Models to the FCA Visualization. SITIS (2013)
Kristoffer Jon Albers, Andreas Leon Aagaard Moth, Morten Mørup, Mikkel N. Schmidt
Large scale inference in the Infinite Relational Model: Gibbs sampling is not enough. MLSP (2013)
Fumiko Kano Glückstad, Tue Herlau, Mikkel N. Schmidt, Morten Mørup, Rafal Rzepka, Kenji Araki
Analysis of Conceptualization Patterns across Groups of People. TAAI (2013)
Tommy S. Alstrøm, Bjørn Sand Jensen, Mikkel N. Schmidt, Natalie V. Kostesha, Jan Larsen
Haussdorff and hellinger for colorimetric sensor array classification. MLSP (2012)
Tue Herlau, Morten Mørup, Mikkel N. Schmidt, Lars Kai Hansen
Modelling dense relational data. MLSP (2012)
Tue Herlau, Morten Mørup, Mikkel N. Schmidt, Lars Kai Hansen
Detecting hierarchical structure in networks. CIP (2012)
Morten Mørup, Mikkel N. Schmidt
Bayesian Community Detection. Neural Comput. 24 (9) (2012)
Morten Mørup, Mikkel N. Schmidt
Transformation invariant sparse coding. MLSP (2011)
Morten Mørup, Mikkel N. Schmidt, Lars Kai Hansen
Infinite Multiple Membership Relational Modeling for Complex Networks CoRR (2011)
Morten Mørup, Mikkel N. Schmidt, Lars Kai Hansen
Infinite multiple membership relational modeling for complex networks. MLSP (2011)
Morten Arngren, Mikkel N. Schmidt, Jan Larsen
Unmixing of Hyperspectral Images using Bayesian Non-negative Matrix Factorization with Volume Prior. J. Signal Process. Syst. 65 (3) (2011)
Mikkel N. Schmidt, Morten Mørup
Infinite non-negative matrix factorization. EUSIPCO (2010)
Mikkel N. Schmidt
Linearly constrained Bayesian matrix factorization for blind source separation. NIPS (2009)
Mikkel N. Schmidt, Shakir Mohamed
Probabilistic non-negative tensor factorization using Markov chain Monte Carlo. EUSIPCO (2009)
Mikkel N. Schmidt, Ole Winther, Lars Kai Hansen
Bayesian Non-negative Matrix Factorization. ICA (2009)
Mikkel N. Schmidt
Function factorization using warped Gaussian processes. ICML (2009)
Mikkel N. Schmidt, Hans Laurberg
Nonnegative Matrix Factorization with Gaussian Process Priors. Comput. Intell. Neurosci. 2008 (2008)
Hans Laurberg, Mikkel N. Schmidt, Mads Græsbøll Christensen, Søren Holdt Jensen
Structured non-negative matrix factorization with sparsity patterns. ACSCC (2008)
Mikkel N. Schmidt, Rasmus Kongsgaard Olsson
Single-channel speech separation using sparse non-negative matrix factorization. INTERSPEECH (2006)
Mikkel N. Schmidt, Morten Mørup
Nonnegative Matrix Factor 2-D Deconvolution for Blind Single Channel Source Separation. ICA (2006)