Johan Kwisthout

Johan Kwisthout

Radboud Universiteit

H-index: 21

Europe-Netherlands

About Johan Kwisthout

Johan Kwisthout, With an exceptional h-index of 21 and a recent h-index of 16 (since 2020), a distinguished researcher at Radboud Universiteit, specializes in the field of Bayesian networks, Approximate Inference, Complexity in PGMs.

His recent articles reflect a diverse array of research interests and contributions to the field:

Cancer Subtype Identification through Integrating Inter and Intra Dataset Relationships in Multi-Omics Data

Neural Population Decoding and Imbalanced Multi-Omic Datasets For Cancer Subtype Diagnosis

Bayesian Integration of Information Using Top-Down Modulated WTA Networks

Differentiating between Bayesian parameter learning and structure learning based on behavioural and pupil measures

Motivating explanations in Bayesian networks using MAP-independence

Parameterized Complexity Results for Bayesian Inference

Statistical learning mechanisms are flexible and can adapt to structural input properties

Parameterized Completeness Results for Bayesian Inference

Johan Kwisthout Information

University

Position

Associate Professor Donders Center for Cognition

Citations(all)

1540

Citations(since 2020)

930

Cited By

998

hIndex(all)

21

hIndex(since 2020)

16

i10Index(all)

40

i10Index(since 2020)

25

Email

University Profile Page

Radboud Universiteit

Google Scholar

View Google Scholar Profile

Johan Kwisthout Skills & Research Interests

Bayesian networks

Approximate Inference

Complexity in PGMs

Top articles of Johan Kwisthout

Title

Journal

Author(s)

Publication Date

Cancer Subtype Identification through Integrating Inter and Intra Dataset Relationships in Multi-Omics Data

IEEE Access

Mark Peelen

Leila Bagheriye

Johan Kwisthout

2024/2/5

Neural Population Decoding and Imbalanced Multi-Omic Datasets For Cancer Subtype Diagnosis

arXiv preprint arXiv:2401.10844

Charles Theodore Kent

Leila Bagheriye

Johan Kwisthout

2024/1/6

Bayesian Integration of Information Using Top-Down Modulated WTA Networks

arXiv preprint arXiv:2308.15390

Otto van der Himst

Leila Bagheriye

Johan Kwisthout

2023/8/29

Differentiating between Bayesian parameter learning and structure learning based on behavioural and pupil measures

PloS one

Danaja Rutar

Olympia Colizoli

Luc Selen

Lukas Spieß

Johan Kwisthout

...

2023/2/16

Motivating explanations in Bayesian networks using MAP-independence

International Journal of Approximate Reasoning

Johan Kwisthout

2023/2/1

Parameterized Complexity Results for Bayesian Inference

arXiv preprint arXiv:2206.07172

Hans Bodlaender

Nils Donselaar

Johan Kwisthout

2022/6/14

Statistical learning mechanisms are flexible and can adapt to structural input properties

Available at SSRN 4027230

Danaja Rutar

Erwin de Wolff

Johan Kwisthout

Sabine Hunnius

2022/12/29

Parameterized Completeness Results for Bayesian Inference

Hans L Bodlaender

Nils Donselaar

Johan Kwisthout

2022/9/19

Structure learning in predictive processing needs revision

Computational Brain & Behavior

Danaja Rutar

Erwin de Wolff

Iris van Rooij

Johan Kwisthout

2022/6

Speeding up approximate MAP by applying domain knowledge about relevant variables

Johan Kwisthout

2022/9/19

From representations in predictive processing to degrees of representational features

Minds and Machines

Danaja Rutar

Wanja Wiese

Johan Kwisthout

2022/9

Differentiating Bayesian model updating and model revision based on their prediction error dynamics

bioRxiv

Danaja Rutar

Olympia Colizoli

Luc Selen

Lukas Spieß

Johan Kwisthout

...

2022/6/17

Certainly Strange: A Probabilistic Perspective on Ignorance

Proceedings of the Annual Meeting of the Cognitive Science Society

Erwin J de Wolff

Iris van Rooij

Johan Kwisthout

2021

Explainable AI using MAP-independence

Johan Kwisthout

2021

Brain-inspired hardware solutions for inference in Bayesian networks

Leila Bagheriye

Johan Kwisthout

2021/12/2

Implementation of Integer Addition on a Spiking Neural Network

Evgeniya Ovchinnikova

JH Geuvers

JHP Kwisthout

2021/6/14

Implementation of a distributed minimum dominating set approximation algorithm in a spiking neural network

V Bosch

A Diehl

D Smits

A Toeter

JHP Kwisthout

2021

On the computational power and complexity of spiking neural networks

Johan Kwisthout

Nils Donselaar

2020/3/17

A Spiking Neuron Implementation of Genetic Algorithms for Optimization

Cao, L.; Kosters, W.; Lijffijt, J.(ed.), Proceedings of the 32nd Benelux Conference on AI (BNAIC'20)

Siegfried Ludwig

Joeri Hartjes

Bram Pol

Gabriela Rivas

Johan Kwisthout

2020

From models of cognition to robot control and back using spiking neural networks

Stefan Iacob

Johan Kwisthout

Serge Thill

2020/7/28

See List of Professors in Johan Kwisthout University(Radboud Universiteit)

Co-Authors

H-index: 83
Anil Seth

Anil Seth

University of Sussex

H-index: 81
Ivan Toni

Ivan Toni

Radboud Universiteit

H-index: 69
Hans L. Bodlaender

Hans L. Bodlaender

Universiteit Utrecht

H-index: 46
Mehdi Dastani

Mehdi Dastani

Universiteit Utrecht

H-index: 40
Peter Lucas

Peter Lucas

Radboud Universiteit

H-index: 32
Theo P. van der Weide

Theo P. van der Weide

Radboud Universiteit

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