Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Mikkel N. Schmidt"'
Autor:
Philip J. H. Jørgensen, Søren F. Nielsen, Jesper L. Hinrich, Mikkel N. Schmidt, Kristoffer H. Madsen, Morten Mørup
Publikováno v:
Entropy, Vol 26, Iss 8, p 697 (2024)
The Parallel Factor Analysis 2 (PARAFAC2) is a multimodal factor analysis model suitable for analyzing multi-way data when one of the modes has incomparable observation units, for example, because of differences in signal sampling or batch sizes. A f
Externí odkaz:
https://doaj.org/article/6eb0fbfff9bc4955b3073e2eaa9a65f4
Autor:
Mikkel N. Schmidt, Daniel Seddig, Eldad Davidov, Morten Mørup, Kristoffer Jon Albers, Jan Michael Bauer, Fumiko Kano Glückstad
Publikováno v:
Methodology, Vol 17, Iss 2, Pp 127-148 (2021)
Latent Profile Analysis (LPA) is a method to extract homogeneous clusters characterized by a common response profile. Previous works employing LPA to human value segmentation tend to select a small number of moderately homogeneous clusters based on m
Externí odkaz:
https://doaj.org/article/7a3a692469824d4b84590ab3ce073642
Autor:
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, Morten Mørup
Publikováno v:
Frontiers in Neuroscience, Vol 16 (2022)
Modern diffusion and functional magnetic resonance imaging (dMRI/fMRI) provide non-invasive high-resolution images from which multi-layered networks of whole-brain structural and functional connectivity can be derived. Unfortunately, the lack of obse
Externí odkaz:
https://doaj.org/article/26bde454eef5404d9724120dfd9b517e
Autor:
Kristoffer J. Albers, Karen S. Ambrosen, Matthew G. Liptrot, Tim B. Dyrby, Mikkel N. Schmidt, Morten Mørup
Publikováno v:
NeuroImage, Vol 238, Iss , Pp 118170- (2021)
The organization of the human brain remains elusive, yet is of great importance to the mechanisms of integrative brain function. At the macroscale, its structural and functional interpretation is conventionally assessed at the level of cortical units
Externí odkaz:
https://doaj.org/article/f3b1162ed38c4037aee1718adb90501e
Autor:
Karen S. Ambrosen, Simon F. Eskildsen, Max Hinne, Kristine Krug, Henrik Lundell, Mikkel N. Schmidt, Marcel A.J. van Gerven, Morten Mørup, Tim B. Dyrby
Publikováno v:
NeuroImage, Vol 204, Iss , Pp 116207- (2020)
Evaluation of the structural connectivity (SC) of the brain based on tractography has mainly focused on the choice of diffusion model, tractography algorithm, and their respective parameter settings. Here, we systematically validate SC derived from a
Externí odkaz:
https://doaj.org/article/a82a5831f2984cf28780758e535ce8d8
Autor:
Kunal Ghosh, Annika Stuke, Milica Todorović, Peter Bjørn Jørgensen, Mikkel N. Schmidt, Aki Vehtari, Patrick Rinke
Publikováno v:
Advanced Science, Vol 6, Iss 9, Pp n/a-n/a (2019)
Abstract Deep learning methods for the prediction of molecular excitation spectra are presented. For the example of the electronic density of states of 132k organic molecules, three different neural network architectures: multilayer perceptron (MLP),
Externí odkaz:
https://doaj.org/article/8366dcb23a5e45a782579f438cd5cc40
Autor:
Muralikrishnan Srinivasan, Jinxiang Song, Alexander Grabowski, Krzysztof Szczerba, Holger K. Iversen, Mikkel N. Schmidt, Darko Zibar, Jochen Schroder, Anders Larsson, Christian Hager, Henk Wymeersch
Publikováno v:
Journal of Lightwave Technology. :1-16
Optical interconnects (OIs) based on vertical-cavity surface-emitting lasers (VCSELs) are the main workhorse within data centers, supercomputers, and even vehicles, providing low-cost, high-rate connectivity. VCSELs must operate under extremely harsh
Publikováno v:
Procedia Computer Science. 201:771-776
Dropout has recently emerged as a powerful and simple method for training neural networks preventing co-adaptation by stochastically omitting neurons. Dropout is currently not grounded in explicit modelling assumptions which so far has precluded its
Publikováno v:
Li, B, Schmidt, M N & Alstrom, T S 2022, ' Raman spectrum matching with contrastive representation learning ', Analyst, vol. 147, no. 10, 2238 . https://doi.org/10.1039/d2an00403h
Raman spectroscopy is an effective, low-cost, non-intrusive technique often used for chemical identification. Typical approaches are based on matching observations to a reference database, which requires careful preprocessing, or supervised machine l