Zobrazeno 1 - 10
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pro vyhledávání: '"Kouw, Wouter M"'
Autor:
Nisslbeck, Tim N., Kouw, Wouter M.
We propose an active inference agent to identify and control a mechanical system with multiple bodies connected by joints. This agent is constructed from multiple scalar autoregressive model-based agents, coupled together by virtue of sharing memorie
Externí odkaz:
http://arxiv.org/abs/2410.10415
Autor:
Kouw, Wouter M.
In nature, active inference agents must learn how observations of the world represent the state of the agent. In engineering, the physics behind sensors is often known reasonably accurately and measurement functions can be incorporated into generativ
Externí odkaz:
http://arxiv.org/abs/2409.01974
We propose a computational procedure for identifying convection in heat transfer dynamics. The procedure is based on a Gaussian process latent force model, consisting of a white-box component (i.e., known physics) for the conduction and linear convec
Externí odkaz:
http://arxiv.org/abs/2407.01226
Autor:
Kouw, Wouter M.
We propose an adaptive model-predictive controller that balances driving the system to a goal state and seeking system observations that are informative with respect to the parameters of a nonlinear autoregressive exogenous model. The controller's ob
Externí odkaz:
http://arxiv.org/abs/2312.15046
We address object tracking by radar and the robustness of the current state-of-the-art methods to process outliers. The standard tracking algorithms extract detections from radar image space to use it in the filtering stage. Filtering is performed by
Externí odkaz:
http://arxiv.org/abs/2209.14397
Autor:
Kouw, Wouter M
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. Here, the nonlinear stochastic differential equation of a Duffing oscilla
Externí odkaz:
http://arxiv.org/abs/2009.00845
Publikováno v:
PLoS ONE 15(8): e0237009, 2020, pp. 1-16
In a broad range of fields it may be desirable to reuse a supervised classification algorithm and apply it to a new data set. However, generalization of such an algorithm and thus achieving a similar classification performance is only possible when t
Externí odkaz:
http://arxiv.org/abs/2002.12105
Publikováno v:
International Conference on Information Processing in Medical Imaging (IPMI), Hong Kong, 2019, pp. 360-371
Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not
Externí odkaz:
http://arxiv.org/abs/1903.04191
Autor:
Kouw, Wouter M., Loog, Marco
Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approach
Externí odkaz:
http://arxiv.org/abs/1901.05335
Autor:
Kouw, Wouter M., Loog, Marco
In machine learning, if the training data is an unbiased sample of an underlying distribution, then the learned classification function will make accurate predictions for new samples. However, if the training data is not an unbiased sample, then ther
Externí odkaz:
http://arxiv.org/abs/1812.11806