The two papers: “Whole Brain Functional Connectivity Predicted by Indirect Structural Connection” and “Modeling Dynamic Functional Connectivity using a Wishart Mixture Model” have been accepted for PRNI2017.
The paper “SCALABLE GROUP LEVEL PROBABILISTIC SPARSE FACTOR ANALYSIS” was presented at ICASSP 2017 conference in session SS-L8: Functional MRI Analysis in a Big Data Era, see also:
The following two papers were presented at MLSP 2016:
The influence of hyper-parameters in the infinite relational model by Kristoffer J. Albers, Morten Mørup, and Mikkel N. Schmidt.
Bayesian latent feature modeling for modeling bipartite networks with overlapping groups by Philip H. Jørgensen, Morten Mørup, Mikkel N. Schmidt, and Tue Herlau.
Poster presented at NIPS 2016 on “Completely random measures for modelling block-structured sparse networks” by Tue Herlau*, Mikkel Schmidt, and Morten Mørup.
link to paper
link to short video presentation
Article on “Quantifying functional connectivity in multi-subject fMRI data using component models” by Kristoffer H. Madsen,
Nathan W. Churchill, and Morten Mørup published in Human Brain Mapping. link to article
Two papers presented at Pattern Recognition and NeuroImaging 2016 (http://prni2016.wix.com/prni2016):
Astrid Engberg, Kasper Andersen, Morten Mørup, Kristoffer Madsen, Independent Vector Analysis for Capturing Common Components in fMRI Group Analysis.
Jesper Hinrich, Søren F. V. Nielsen, Kristoffer Madsen, Morten Mørup, Variational Group-PCA for Intrinsic Dimensionality Determination in fMRI Data.