Kristoffer Jon Albers

Kristoffer Jon Albers

PhD student

DTU COMPUTE
Department of Applied Mathematics and Computer Science

Technical University of Denmark

E-mail kjal@dtu.dk

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2016
 

The influence of hyper-parameters in the infinite relational model

Albers, Kristoffer Jon ; Mørup, Morten ; Schmidt, Mikkel Nørgaard
part of: Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2016) (ISBN: 978-1-5090-0746-2), 2016, IEEE,
Presented at:
26th IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2016), 2016, Salerno

Type: Article in proceedings (Peer reviewed)

Status: Published     |    Year: 2016     |    DOI: http://dx.doi.org/10.1109/MLSP.2016.7738908

2015
  PDF

Numerical approximations for speeding up mcmc inference in the infinite relational model

Schmidt, Mikkel Nørgaard ; Albers, Kristoffer Jon
part of: Proceedings of the 23rd European Signal Processing Conference (EUSIPCO 2015) (ISBN: 978-0-9928626-3-3), pages: 2781-2785, 2015, IEEE,
Presented at:
23rd European Signal Processing Conference (EUSIPCO 2015), 2015, Nice

Type: Article in proceedings (Peer reviewed)

Status: Published     |    Year: 2015     |    DOI: http://dx.doi.org/10.1109/EUSIPCO.2015.7362891

2014
 

Nonparametric Bayesian Clustering of Structural Whole Brain Connectivity in Full Image Resolution

Ambrosen, Karen Marie Sandø ; Albers, Kristoffer Jon ; Dyrby, Tim B. ; Schmidt, Mikkel Nørgaard ; Mørup, Morten
part of: Proceedings of the 4th International Workshop on Pattern Recognition in Neuroimaging (PRNI 2014) (ISBN: 978-1-4799-4149-0), 2014, IEEE,
Presented at:
4th International Workshop on Pattern Recognition in Neuroimaging, 2014, Tübingen

Type: Article in proceedings (Peer reviewed)

Status: Published     |    Year: 2014

2013
 

Large scale inference in the Infinite Relational Model: Gibbs sampling is not enough

Albers, Kristoffer Jon ; Moth, Andreas Leon Aagard ; Mørup, Morten ; Schmidt, Mikkel Nørgaard
part of: 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2013, IEEE,
Presented at:
2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2013, Southampton

Type: Article in proceedings (Peer reviewed)

Status: Published     |    Year: 2013     |    DOI: http://dx.doi.org/10.1109/MLSP.2013.6661904