Search Results for author: Wouter M. Kouw

Found 14 papers, 11 papers with code

Online system identification in a Duffing oscillator by free energy minimisation

1 code implementation2 Sep 2020 Wouter M. Kouw

Online system identification is the estimation of parameters of a dynamical system, such as mass or friction coefficients, for each measurement of the input and output signals.

Bayesian joint state and parameter tracking in autoregressive models

no code implementations L4DC 2020 Ismail Senoz, Albert Podusenko, Wouter M. Kouw, Bert de Vries

We address the problem of online Bayesian state and parameter tracking in autoregressive (AR) models with time-varying process noise variance.

A cross-center smoothness prior for variational Bayesian brain tissue segmentation

1 code implementation11 Mar 2019 Wouter M. Kouw, Silas N. Ørting, Jens Petersen, Kim S. Pedersen, Marleen de Bruijne

Here we present a smoothness prior that is fit to segmentations produced at another medical center.

A review of domain adaptation without target labels

1 code implementation16 Jan 2019 Wouter M. Kouw, Marco Loog

Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure.

Domain Generalization Unsupervised Domain Adaptation

An introduction to domain adaptation and transfer learning

no code implementations31 Dec 2018 Wouter M. Kouw, Marco Loog

Domain adaptation and transfer learning are sub-fields within machine learning that are concerned with accounting for these types of changes.

Domain Adaptation Machine Learning

Learning an MR acquisition-invariant representation using Siamese neural networks

1 code implementation17 Oct 2018 Wouter M. Kouw, Marco Loog, Wilbert Bartels, Adriënne M. Mendrik

Generalization of voxelwise classifiers is hampered by differences between MRI-scanners, e. g. different acquisition protocols and field strengths.

Target Robust Discriminant Analysis

1 code implementation21 Jun 2018 Wouter M. Kouw, Marco Loog

In practice, the data distribution at test time often differs, to a smaller or larger extent, from that of the original training data.

Effects of sampling skewness of the importance-weighted risk estimator on model selection

1 code implementation19 Apr 2018 Wouter M. Kouw, Marco Loog

For sample selection bias settings, and for small sample sizes, the importance-weighted risk estimator produces overestimates for datasets in the body of the sampling distribution, i. e. the majority of cases, and large underestimates for data sets in the tail of the sampling distribution.

Model Selection Selection bias

Robust importance-weighted cross-validation under sample selection bias

1 code implementation17 Oct 2017 Wouter M. Kouw, Jesse H. Krijthe, Marco Loog

Cross-validation under sample selection bias can, in principle, be done by importance-weighting the empirical risk.

General Classification Selection bias

MR Acquisition-Invariant Representation Learning

1 code implementation22 Sep 2017 Wouter M. Kouw, Marco Loog, Lambertus W. Bartels, Adriënne M. Mendrik

Due to this acquisition related variation, classifiers trained on data from a specific scanner fail or under-perform when applied to data that was acquired differently.

Classification General Classification +1

Target contrastive pessimistic risk for robust domain adaptation

1 code implementation25 Jun 2017 Wouter M. Kouw, Marco Loog

In domain adaptation, classifiers with information from a source domain adapt to generalize to a target domain.

Domain Adaptation Selection bias

On Regularization Parameter Estimation under Covariate Shift

1 code implementation31 Jul 2016 Wouter M. Kouw, Marco Loog

This paper identifies a problem with the usual procedure for L2-regularization parameter estimation in a domain adaptation setting.

Domain Adaptation L2 Regularization

Feature-Level Domain Adaptation

no code implementations15 Dec 2015 Wouter M. Kouw, Jesse H. Krijthe, Marco Loog, Laurens J. P. van der Maaten

Our empirical evaluation of FLDA focuses on problems comprising binary and count data in which the transfer can be naturally modeled via a dropout distribution, which allows the classifier to adapt to differences in the marginal probability of features in the source and the target domain.

Domain Adaptation

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