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ORCIDMikkel N. Schmidt
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2020 – today
- 2024
[j15]Philip J. H. Jørgensen
, Søren Føns Vind Nielsen, Jesper Løve Hinrich, Mikkel N. Schmidt, Kristoffer H. Madsen, Morten Mørup:
Probabilistic PARAFAC2. Entropy 26(8): 697 (2024)- [j14]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) - [c42]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 - [i14]Thea Brüsch, Kristoffer K. Wickstrøm, Mikkel N. Schmidt, Tommy S. Alstrøm, Robert Jenssen:
Explaining time series models using frequency masking. CoRR abs/2406.13584 (2024) - [i13]Anna Emilie J. Wedenborg, Michael Alexander Harborg, Andreas Bigom, Oliver Elmgreen, Marcus Presutti, Andreas Råskov, Fumiko Kano Glückstad, Mikkel N. Schmidt, Morten Mørup:
Modeling Human Responses by Ordinal Archetypal Analysis. CoRR abs/2409.07934 (2024) - 2023
- [c41]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: 3964-3973 - [c40]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: 1-5 - [c39]Thea Brüsch, Mikkel N. Schmidt, Tommy S. Alstrøm:
Multi-View Self-Supervised Learning For Multivariate Variable-Channel Time Series. MLSP 2023: 1-6 - [c38]David Frich Hansen, Tommy Sonne Alstrøm, Mikkel N. Schmidt:
Amortized Variational Peak Fitting For Spectroscopic Data. MLSP 2023: 1-6 - [i12]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 abs/2305.16325 (2023) - [i11]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 abs/2306.13263 (2023) - [i10]Thea Brüsch, Mikkel N. Schmidt, Tommy S. Alstrøm:
Multi-view self-supervised learning for multivariate variable-channel time series. CoRR abs/2307.09614 (2023) - [i9]Peter Bjørn Jørgensen, Jonas Busk, Ole Winther, Mikkel N. Schmidt:
Coherent energy and force uncertainty in deep learning force fields. CoRR abs/2312.04174 (2023) - 2022
- [j13]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): 15012 (2022) - [c37]Tue Herlau, Mikkel N. Schmidt, Morten Mørup:
Bayesian dropout. ANT/EDI40 2022: 771-776 - [i8]Rasmus Larsen, Mikkel Nørgaard Schmidt:
Programmatic Policy Extraction by Iterative Local Search. CoRR abs/2201.06863 (2022) - [i7]Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm:
Raman Spectrum Matching with Contrastive Representation Learning. CoRR abs/2202.12549 (2022) - [i6]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 abs/2211.14481 (2022) - [i5]Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich:
Partial Variance Reduction improves Non-Convex Federated learning on heterogeneous data. CoRR abs/2212.02191 (2022) - 2021
- [j12]Rasmus Bonnevie, Mikkel N. Schmidt:
Matrix Product States for Inference in Discrete Probabilistic Models. J. Mach. Learn. Res. 22: 187:1-187:48 (2021) - [j11]Kristoffer Jon Albers, Karen Sandø Ambrosen, Matthew G. Liptrot, Tim B. Dyrby, Mikkel N. Schmidt, Morten Mørup:
Using connectomics for predictive assessment of brain parcellations. NeuroImage 238: 118170 (2021) - [c36]Rasmus Larsen, Mikkel Nørgaard Schmidt:
Programmatic Policy Extraction by Iterative Local Search. ILP 2021: 156-166 - [i4]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 abs/2107.06068 (2021) - 2020
- [j10]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:
Validation of structural brain connectivity networks: The impact of scanning parameters. NeuroImage 204 (2020)
2010 – 2019
- 2019
- [c35]Mikkel N. Schmidt, Tommy S. Alstrøm, Marcus Svendstorp, Jan Larsen:
Peak Detection and Baseline Correction Using a Convolutional Neural Network. ICASSP 2019: 2757-2761 - [c34]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: 1-6 - 2018
- [j9]Søren Føns Vind Nielsen, Mikkel N. Schmidt, Kristoffer Henriksen, Morten Mørup:
Predictive assessment of models for dynamic functional connectivity. NeuroImage 171: 116-134 (2018) - [c33]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: 159-164 - [c32]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: 1-4 - [i3]Peter Bjørn Jørgensen, Karsten Wedel Jacobsen, Mikkel N. Schmidt:
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials. CoRR abs/1806.03146 (2018) - [i2]Philip J. H. Jørgensen, Søren Føns Vind Nielsen, Jesper Løve Hinrich, Mikkel N. Schmidt, Kristoffer Hougaard Madsen, Morten Mørup:
Probabilistic PARAFAC2. CoRR abs/1806.08195 (2018) - 2017
- [j8]Rasmus Erbou Røge, Kristoffer Hougaard Madsen, Mikkel N. Schmidt, Morten Mørup:
Infinite von Mises-Fisher Mixture Modeling of Whole Brain fMRI Data. Neural Comput. 29(10): 2712-2741 (2017) - [c31]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: 2317-2321 - [c30]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: 6314-6318 - [c29]Rasmus Bonnevie, Mikkel N. Schmidt, Morten Mørup:
Difference-of-Convex optimization for variational kl-corrected inference in dirichlet process mixtures. MLSP 2017: 1-6 - [c28]Søren Føns Vind Nielsen, Kristoffer Hougaard Madsen, Mikkel N. Schmidt, Morten Mørup:
Modeling dynamic functional connectivity using a wishart mixture model. PRNI 2017: 1-4 - 2016
- [c27]Kristoffer Jon Albers, Morten Mørup, Mikkel N. Schmidt:
The influence of hyper-parameters in the infinite relational model. MLSP 2016: 1-6 - [c26]Philip H. Jørgensen, Morten Mørup, Mikkel N. Schmidt, Tue Herlau:
Bayesian latent feature modeling for modeling bipartite networks with overlapping groups. MLSP 2016: 1-6 - [c25]Tue Herlau, Mikkel N. Schmidt, Morten Mørup:
Completely random measures for modelling block-structured sparse networks. NIPS 2016: 4260-4268 - 2015
- [c24]Mikkel N. Schmidt, Kristoffer Jon Albers:
Numerical approximations for speeding up MCMC inference in the infinite relational model. EUSIPCO 2015: 2781-2785 - [c23]Rasmus Erbou Røge, Kristoffer Hougaard Madsen, Mikkel N. Schmidt, Morten Mørup:
Unsupervised segmentation of task activated regions in fMRI. MLSP 2015: 1-6 - 2014
- [j7]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): 61-108 (2014) - [j6]Morten Mørup, Mikkel N. Schmidt:
Errata to "Bayesian Community Detection" (Neural Computation, Sept. 2012 , Vol. 24, No. 9: 2434-2456). Neural Comput. 26(6): 1236-1237 (2014) - [j5]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: 301-315 (2014) - [c22]Mikkel N. Schmidt, Tue Herlau, Morten Mørup:
Discovering hierarchical structure in normal relational data. CIP 2014: 1-6 - [c21]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: 1-6 - [c20]Morten Mørup, Fumiko Kano Glückstad, Tue Herlau, Mikkel N. Schmidt:
Nonparametric statistical structuring of knowledge systems using binary feature matches. MLSP 2014: 1-6 - [c19]Karen Sandø 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: 1-4 - 2013
- [j4]Mikkel N. Schmidt, Morten Mørup:
Nonparametric Bayesian Modeling of Complex Networks: An Introduction. IEEE Signal Process. Mag. 30(3): 110-128 (2013) - [c18]Tue Herlau, Morten Mørup, Mikkel N. Schmidt:
Modeling Temporal Evolution and Multiscale Structure in Networks. ICML (3) 2013: 960-968 - [c17]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: 1-6 - [c16]Karen Sandø Ambrosen, Tue Herlau, Tim B. Dyrby, Mikkel N. Schmidt, Morten Mørup:
Comparing Structural Brain Connectivity by the Infinite Relational Model. PRNI 2013: 50-53 - [c15]Fumiko Kano Glückstad, Tue Herlau, Mikkel N. Schmidt, Morten Mørup:
Unsupervised Knowledge Structuring: Application of Infinite Relational Models to the FCA Visualization. SITIS 2013: 233-240 - [c14]Fumiko Kano Glückstad, Tue Herlau, Mikkel N. Schmidt, Morten Mørup, Rafal Rzepka, Kenji Araki:
Analysis of Conceptualization Patterns across Groups of People. TAAI 2013: 349-354 - 2012
- [j3]Morten Mørup, Mikkel N. Schmidt:
Bayesian Community Detection. Neural Comput. 24(9): 2434-2456 (2012) - [c13]Tue Herlau, Morten Mørup, Mikkel N. Schmidt, Lars Kai Hansen:
Detecting hierarchical structure in networks. CIP 2012: 1-6 - [c12]Tommy S. Alstrøm, Bjørn Sand Jensen, Mikkel N. Schmidt, Natalie V. Kostesha, Jan Larsen:
Haussdorff and hellinger for colorimetric sensor array classification. MLSP 2012: 1-6 - [c11]Tue Herlau, Morten Mørup, Mikkel N. Schmidt, Lars Kai Hansen:
Modelling dense relational data. MLSP 2012: 1-6 - 2011
- [j2]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): 479-496 (2011) - [c10]Morten Mørup, Mikkel N. Schmidt:
Transformation invariant sparse coding. MLSP 2011: 1-6 - [c9]Morten Mørup, Mikkel N. Schmidt, Lars Kai Hansen:
Infinite multiple membership relational modeling for complex networks. MLSP 2011: 1-6 - [i1]Morten Mørup, Mikkel N. Schmidt, Lars Kai Hansen:
Infinite Multiple Membership Relational Modeling for Complex Networks. CoRR abs/1101.5097 (2011) - 2010
- [c8]Mikkel N. Schmidt, Morten Mørup:
Infinite non-negative matrix factorization. EUSIPCO 2010: 905-909
2000 – 2009
- 2009
- [c7]Mikkel N. Schmidt, Shakir Mohamed:
Probabilistic non-negative tensor factorization using Markov chain Monte Carlo. EUSIPCO 2009: 1918-1922 - [c6]Mikkel N. Schmidt:
Function factorization using warped Gaussian processes. ICML 2009: 921-928 - [c5]Mikkel N. Schmidt, Ole Winther, Lars Kai Hansen:
Bayesian Non-negative Matrix Factorization. ICA 2009: 540-547 - [c4]Mikkel N. Schmidt:
Linearly constrained Bayesian matrix factorization for blind source separation. NIPS 2009: 1624-1632 - 2008
- [j1]Mikkel N. Schmidt, Hans Laurberg:
Nonnegative Matrix Factorization with Gaussian Process Priors. Comput. Intell. Neurosci. 2008 (2008) - [c3]Hans Laurberg, Mikkel N. Schmidt, Mads Græsbøll Christensen, Søren Holdt Jensen:
Structured non-negative matrix factorization with sparsity patterns. ACSCC 2008: 1693-1697 - 2006
- [c2]Mikkel N. Schmidt, Morten Mørup:
Nonnegative Matrix Factor 2-D Deconvolution for Blind Single Channel Source Separation. ICA 2006: 700-707 - [c1]Mikkel N. Schmidt, Rasmus Kongsgaard Olsson:
Single-channel speech separation using sparse non-negative matrix factorization. INTERSPEECH 2006
Coauthor Index
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last updated on 2024-10-23 20:35 CEST by the dblp team
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