Search Results - "Heskes, T."

Self-organizing maps, vector quantization, and mixture modeling by Heskes, T.

“…Self-organizing maps are popular algorithms for unsupervised learning and data visualization. Exploiting the link between vector quantization and mixture…”
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Convexity Arguments for Efficient Minimization of the Bethe and Kikuchi Free Energies by Heskes, T.

“…Loopy and generalized belief propagation are popular algorithms for approximate inference in Markov random fields and Bayesian networks. Fixed points of these…”
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Role of conduct problems in the relation between Attention-Deficit Hyperactivity disorder, substance use, and gaming by Schoenmacker, G.H., Groenman, A.P., Sokolova, E., Oosterlaan, J., Rommelse, N., Roeyers, H., Oades, R.D., Faraone, S.V., Franke, B., Heskes, T., Arias Vasquez, A., Claassen, T., Buitelaar, J.K.

“…Known comorbidities for Attention-Deficit Hyperactivity Disorder (ADHD) include conduct problems, substance use disorder and gaming. Comorbidity with conduct…”
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Non-uniform patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation by Ghafoorian, M., Karssemeijer, N., Heskes, T., van Uder, I. W. M., de Leeuw, F. E., Marchiori, E., van Ginneken, B., Platel, B.

“…Convolutional neural networks (CNN) have been widely used for visual recognition tasks including semantic segmentation of images. While the existing methods…”
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Properties of Bethe Free Energies and Message Passing in Gaussian Models by Cseke, B., Heskes, T.

“…We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. We define…”
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Hierarchical visualization of time-series data using switching linear dynamical systems by Zoeter, O., Heskes, T.

“…We propose a novel visualization algorithm for high-dimensional time-series data. In contrast to most visualization techniques, we do not assume consecutive…”
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Partial retraining: a new approach to input relevance determination by van de Laar, P, Heskes, T, Gielen, S

“…In this article we introduce partial retraining, an algorithm to determine the relevance of the input variables of a trained neural network. We place this…”
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Towards individualized monitoring of cognition in multiple sclerosis in the digital era: A one-year cohort study by Lam, Ka-Hoo, Bucur, Ioan Gabriel, Van Oirschot, Pim, De Graaf, Frank, Weda, Hans, Strijbis, Eva, Uitdehaag, Bernard, Heskes, Tom, Killestein, Joep, De Groot, Vincent

“…•Frequent cognition tests in the ambulant setting could enhance clinical assessment.•Weekly smartphone tests enable higher temporal resolution and individual…”
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Clustering ensembles of neural network models by Bakker, Bart, Heskes, Tom

“…We show that large ensembles of (neural network) models, obtained e.g. in bootstrapping or sampling from (Bayesian) probability distributions, can be…”
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On the decoding of intracranial data using sparse orthonormalized partial least squares by van Gerven, Marcel A J, Chao, Zenas C, Heskes, Tom

“…It has recently been shown that robust decoding of motor output from electrocorticogram signals in monkeys over prolonged periods of time has become feasible…”
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On 'natural' learning and pruning in multi-layered perceptrons by Heskes, T

“…Several studies have shown that natural gradient descent for on-line learning is much more efficient than standard gradient descent. In this article, we derive…”
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Fault Trees, Decision Trees, And Binary Decision Diagrams: A Systematic Comparison by Jimenez-Roa, L. A, Heskes, T, Stoelinga, M

“…In reliability engineering, we need to understand system dependencies, cause-effect relations, identify critical components, and analyze how they trigger…”
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Novel approximations for inference in nonlinear dynamical systems using expectation propagation by Ypma, Alexander, Heskes, Tom

“…We formulate the problem of inference in nonlinear dynamical systems in the framework of expectation propagation, and propose two novel algorithms. The first…”
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Predicting Preference Judgments of Individual Normal and Hearing-Impaired Listeners With Gaussian Processes by Groot, P C, Heskes, T, Dijkstra, T M H, Kates, J M

“…A probabilistic kernel approach to pairwise preference learning based on Gaussian processes is applied to predict preference judgments for sound quality…”
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A theoretical comparison of batch-mode, on-line, cyclic, and almost-cyclic learning by Heskes, T., Wiegerinck, W.

“…We study and compare different neural network learning strategies: batch-mode learning, online learning, cyclic learning, and almost-cyclic learning…”
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Improving Cox survival analysis with a neural-Bayesian approach by Bakker, Bart, Heskes, Tom, Neijt, Jan, Kappen, Bert

“…In this article we show that traditional Cox survival analysis can be improved upon when supplemented with sensible priors and analysed within a neural…”
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Iterated extended Kalman smoothing with expectation-propagation by Ypma, A., Heskes, T.

“…We formulate extended Kalman smoothing in an expectation-propagation (EP) framework. The approximation involved (a local linearization) can be looked upon as a…”
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Deterioration modeling of sewer pipes via discrete-time Markov chains: A large-scale case study in the Netherlands by Jimenez-Roa, L. A, Heskes, T, Tinga, T, Molegraaf, H, Stoelinga, M

“…Sewer pipe network systems are an important part of civil infrastructure, and in order to find a good trade-off between maintenance costs and system…”
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EM algorithms for self-organizing maps by Heskes, T., Spanjers, J.-J., Wiegerinck, W.

“…Self-organizing maps are popular algorithms for unsupervised learning and data visualization. Exploiting the link between vector quantization and mixture…”
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Predicting carcinoid heart disease with the noisy-threshold classifier by van Gerven, Marcel A.J, Jurgelenaite, Rasa, Taal, Babs G, Heskes, Tom, Lucas, Peter J.F

“…Summary Objective To predict the development of carcinoid heart disease (CHD), which is a life-threatening complication of certain neuroendocrine tumors. To…”
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