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Personal profile

Research profile

I am interested in robots, brains and robotbrains. Intelligent behaviour fascinates me: how animals can efficiently process a flood of sensory signals and learn to take actions to survive. I am passionate about taking lessons learned from how brains process information, to making intelligent machines.

My work in the Bayesian Intelligent Autonomous Systems lab focuses on making active inference agents more efficient. Our first steps consists of making individual nodes in factor graphs decide for themselves if they will communicate a signal forward, based on how much the message reduces free energy.

Previous projects include domain-adaptive machine learning and model validation under covariate shift. Mainly, I designed and analyzed maximum-likelihood and minimax estimators for statistical models, which have been applied to image, signal and natural language processing problems.

Education/Academic qualification

Doctor, Delft University of Technology

1 Sep 201331 Aug 2017

Master, Maastricht University

1 Sep 201131 Aug 2013

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2016 2019

  • 10 Citations
  • 4 Conference contribution
  • 3 Article

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

Kouw, W. M., Ørting, S. N., Petersen, J., Pedersen, K. S. & de Bruijne, M., 1 Jan 2019, International Conference on Information Processing in Medical Imaging. Bao, S., Chung, A. C. S., Gee, J. C. & Yushkevich, P. A. (eds.). Cham: Springer, p. 360-371 12 p. (Lecture Notes in Computer Science; vol. 11492 LNCS)

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Open Access
File
Brain
Tissue
Classifiers

A review of domain adaptation without target labels

Kouw, W. M. & Loog, M., 7 Oct 2019, In : IEEE Transactions on Pattern Analysis and Machine Intelligence.

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
File
Labels
Classifiers
Target
Parameter estimation
Learning systems

Learning an MR acquisition-invariant representation using Siamese neural networks

Kouw, W. M., Loog, M., Bartels, L. W. & Mendrik, A. M., Jul 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). Piscataway: Institute of Electrical and Electronics Engineers, p. 364-367 4 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Open Access
File
Classifiers
Neural networks
Magnetic resonance imaging
Tissue
Experiments
2 Citations (Scopus)

CT image segmentation of bone for medical additive manufacturing using a convolutional neural network

Minnema, J., van Eijnatten, M., Kouw, W., Diblen, F., Mendrik, A. & Wolff, J., 1 Dec 2018, In : Computers in Biology and Medicine. 103, p. 130-139 10 p.

Research output: Contribution to journalArticleAcademicpeer-review

3D printers
Image segmentation
Tomography
Bone
Neural networks

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

Kouw, W. & Loog, M., 26 Nov 2018, International Conference on Pattern Recognition. Piscataway: Institute of Electrical and Electronics Engineers, p. 1468-1473 6 p. 8546186

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Open Access
Sampling
Computational complexity

Prizes

Niels Stensen Fellow

Wouter Kouw (Recipient), Oct 2017

Recognition: OtherFellowships & membershipsScientific

university
experience