| Community-based influence maximization in social networks under a competitive linear threshold model A Bozorgi, S Samet, J Kwisthout, T Wareham Knowledge-Based Systems 134, 149-158, 2017 | 138 | 2017 |
| Most probable explanations in Bayesian networks: Complexity and tractability J Kwisthout International Journal of Approximate Reasoning 52 (9), 1452-1469, 2011 | 120 | 2011 |
| Beta-and gamma-band activity reflect predictive coding in the processing of causal events S van Pelt, L Heil, J Kwisthout, S Ondobaka, I van Rooij, H Bekkering Social cognitive and affective neuroscience 11 (6), 973-980, 2016 | 107 | 2016 |
| Bayesian intractability is not an ailment that approximation can cure. J Kwisthout, T Wareham, I Van Rooij Cogn. Sci. 35 (5), 779-784, 2011 | 101 | 2011 |
| Cognition and intractability: A guide to classical and parameterized complexity analysis I Van Rooij, M Blokpoel, J Kwisthout, T Wareham Cambridge University Press, 2019 | 97 | 2019 |
| To be precise, the details don’t matter: On predictive processing, precision, and level of detail of predictions J Kwisthout, H Bekkering, I Van Rooij Brain and cognition 112, 84-91, 2017 | 93 | 2017 |
| The necessity of bounded treewidth for efficient inference in Bayesian networks JHP Kwisthout, HL Bodlaender, LC van der Gaag ECAI 2010, 237-242, 2010 | 92 | 2010 |
| Intentional communication: Computationally easy or difficult? I Van Rooij, J Kwisthout, M Blokpoel, J Szymanik, T Wareham, I Toni Frontiers in Human Neuroscience 5, 52, 2011 | 71 | 2011 |
| Bridging the gap between theory and practice of approximate Bayesian inference J Kwisthout, I Van Rooij Cognitive Systems Research 24, 2-8, 2013 | 56 | 2013 |
| Joint attention and language evolution J Kwisthout, P Vogt, P Haselager, T Dijkstra Connection Science 20 (2-3), 155-171, 2008 | 49 | 2008 |
| Perception is in the details: A predictive coding account of the psychedelic phenomenon S Pink-Hashkes, I van Rooij, J Kwisthout Proceedings of the Annual Meeting of the Cognitive Science Society 39, 2017 | 44 | 2017 |
| Computational resource demands of a predictive Bayesian brain J Kwisthout, I Van Rooij Computational Brain & Behavior 3 (2), 174-188, 2020 | 42 | 2020 |
| Structural properties as proxy for semantic relevance in RDF graph sampling L Rietveld, R Hoekstra, S Schlobach, C Guéret International Semantic Web Conference, 81-96, 2014 | 42 | 2014 |
| Rational analysis, intractability, and the prospects of ‘as if’-explanations I van Rooij, CD Wright, J Kwisthout, T Wareham Synthese 195, 491-510, 2018 | 37 | 2018 |
| Can infants' sense of agency be found in their behavior? Insights from babybot simulations of the mobile-paradigm L Zaadnoordijk, M Otworowska, J Kwisthout, S Hunnius Cognition 181, 58-64, 2018 | 36 | 2018 |
| The computational complexity of probabilistic networks JHP Kwisthout Utrecht University, 2009 | 32 | 2009 |
| When can predictive brains be truly Bayesian? M Blokpoel, J Kwisthout, I van Rooij Frontiers in psychology 3, 406, 2012 | 29 | 2012 |
| The computational complexity of sensitivity analysis and parameter tuning J Kwisthout, LC Van Der Gaag arXiv preprint arXiv:1206.3265, 2012 | 27 | 2012 |
| On the computational power and complexity of spiking neural networks J Kwisthout, N Donselaar Proceedings of the 2020 Annual Neuro-Inspired Computational Elements …, 2020 | 26 | 2020 |
| A computational-level explanation of the speed of goal inference M Blokpoel, J Kwisthout, TP van der Weide, T Wareham, I van Rooij Journal of Mathematical Psychology 57 (3-4), 117-133, 2013 | 25 | 2013 |
Iris van RooijRadboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour確認したメール アドレス: donders.ru.nl
