Probabilistic Spatial Transformer Networks

1 code implementation • 7 Apr 2020 • Pola Schwöbel, Frederik Warburg, Martin Jørgensen, Kristoffer H. Madsen, Søren Hauberg

Spatial Transformer Networks (STNs) estimate image transformations that can improve downstream tasks by `zooming in' on relevant regions in an image.

Data Augmentation Time Series +2

Probabilistic PARAFAC2

1 code implementation • 21 Jun 2018 • Philip J. H. Jørgensen, Søren F. V. Nielsen, Jesper L. Hinrich, Mikkel N. Schmidt, Kristoffer H. Madsen, Morten Mørup

The 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.

Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring

no code implementations • 2 Oct 2017 • Albert Vilamala, Kristoffer H. Madsen, Lars K. Hansen

Sleep studies are important for diagnosing sleep disorders such as insomnia, narcolepsy or sleep apnea.

EEG Sleep Stage Detection +1

Scalable Group Level Probabilistic Sparse Factor Analysis

no code implementations • 14 Dec 2016 • Jesper L. Hinrich, Søren F. V. Nielsen, Nicolai A. B. Riis, Casper T. Eriksen, Jacob Frøsig, Marco D. F. Kristensen, Mikkel N. Schmidt, Kristoffer H. Madsen, Morten Mørup

Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation.

Experimental Design

Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data

1 code implementation • 4 Jan 2016 • Søren F. V. Nielsen, Kristoffer H. Madsen, Rasmus Røge, Mikkel N. Schmidt, Morten Mørup

We further investigate what drives dynamic states using the model on the entire data collated across subjects and task/rest.

Clustering EEG