Kristian Kersting

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Luc De Raedtprofessor, KU Leuven & guestprofessor, Örebro UniversityEmail verificata su cs.kuleuven.be
Sriraam NatarajanUniversity of Texas at DallasEmail verificata su utdallas.edu
Christian BauckhageProf. of Computer Science, University of Bonn, Fraunhofer IAISEmail verificata su bit.uni-bonn.de
Mirwaes WahabzadaBASF Digital Farming GmbHEmail verificata su basf.com
Alejandro MolinaTU DarmstadtEmail verificata su cs.tu-darmstadt.de
Tushar KhotAllen Institute for AI; University of Wisconsin-MadisonEmail verificata su cs.wisc.edu
Marion NeumannWashington University in St. LouisEmail verificata su wustl.edu
Fabian Hadijigoedle.io GmbHEmail verificata su goedle.io
Christian ThurauN26Email verificata su n26.com
Jude ShavlikProfessor of Computer Science and of Biostatistics & Medical Informatics, University of WisconsinEmail verificata su cs.wisc.edu
Wolfram BurgardProfessor of Computer Science, University of FreiburgEmail verificata su informatik.uni-freiburg.de
Anne-Katrin MahleinInstitute of Sugar Beet Reserach, GöttingenEmail verificata su ifz-goettingen.de
Christian PlagemannGoogle ResearchEmail verificata su google.com
Babak AhmadiFraunhofer IAISEmail verificata su iais.fraunhofer.de
Martin MladenovGoogle ResearchEmail verificata su google.com
Zhao XuNEC Labs EuropeEmail verificata su acm.org
Petra MutzelProfessorin für Informatik, Universität BonnEmail verificata su cs.uni-bonn.de
Antonio VergariLecturer, University of Edinburgh, UKEmail verificata su ed.ac.uk
Robert PeharzAssistant Professor of AI, TU GrazEmail verificata su tugraz.at
Christopher MorrisPostdoc at McGill and MilaEmail verificata su christophermorris.info

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Kristian Kersting

Professor of AI & Machine Learning, TU Darmstadt, Co-Director of hessian.ai, Germany, CLAIRE & ELLIS

Email verificata su cs.tu-darmstadt.de - Home page

Artificial Intelligence Neurosymbolic AI Probabilistic Circuits Interpretability Machine Learning


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
Introduction to statistical relational learning D Koller, N Friedman, S Džeroski, C Sutton, A McCallum, A Pfeffer, ... MIT press, 2007	1876	2007
Probabilistic Inductive Logic Programming L De Raedt, K Kersting Probabilistic Inductive Logic Programming - Theory and Applications, 1-27, 2008	534	2008
Most likely heteroscedastic Gaussian process regression K Kersting, C Plagemann, P Pfaff, W Burgard Proceedings of the 24th international conference on Machine learning, 393-400, 2007	351	2007
Bayesian logic programs K Kersting, L De Raedt arXiv preprint cs/0111058, 2001	317	2001
Statistical relational artificial intelligence: Logic, probability, and computation LD Raedt, K Kersting, S Natarajan, D Poole Synthesis lectures on artificial intelligence and machine learning 10 (2), 1-189, 2016	250	2016
Lifted Probabilistic Inference with Counting Formulas. B Milch, LS Zettlemoyer, K Kersting, M Haimes, LP Kaelbling Aaai 8, 1062-1068, 2008	232	2008
Probabilistic logic learning L De Raedt, K Kersting ACM SIGKDD Explorations Newsletter 5 (1), 31-48, 2003	219	2003
Benchmark data sets for graph kernels K Kersting, NM Kriege, C Morris, P Mutzel, M Neumann URL http://graphkernels. cs. tu-dortmund. de 29, 2016	211	2016
Bayesian Logic Programming: Theory and Tool K Kersting, L De Raedt Introduction to Statistical Relational Learning, 291, 2007	211	2007
Counting belief propagation K Kersting, B Ahmadi, S Natarajan arXiv preprint arXiv:1205.2637, 2012	184	2012
Towards combining inductive logic programming with Bayesian networks K Kersting, LD Raedt International Conference on Inductive Logic Programming, 118-131, 2001	180	2001
Predicting player churn in the wild F Hadiji, R Sifa, A Drachen, C Thurau, K Kersting, C Bauckhage 2014 IEEE Conference on Computational Intelligence and Games, 1-8, 2014	177	2014
Propagation kernels: efficient graph kernels from propagated information M Neumann, R Garnett, C Bauckhage, K Kersting Machine Learning 102 (2), 209-245, 2016	172	2016
Tudataset: A collection of benchmark datasets for learning with graphs C Morris, NM Kriege, F Bause, K Kersting, P Mutzel, M Neumann arXiv preprint arXiv:2007.08663, 2020	166	2020
Gradient-based boosting for statistical relational learning: The relational dependency network case S Natarajan, T Khot, K Kersting, B Gutmann, J Shavlik Machine Learning 86 (1), 25-56, 2012	164	2012
Bellman goes relational K Kersting, MV Otterlo, L De Raedt Proceedings of the twenty-first international conference on Machine learning, 59, 2004	154	2004
Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis C Römer, M Wahabzada, A Ballvora, F Pinto, M Rossini, C Panigada, ... Functional Plant Biology 39 (11), 878-890, 2012	153	2012
Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions M Kuska, M Wahabzada, M Leucker, HW Dehne, K Kersting, EC Oerke, ... Plant methods 11 (1), 1-15, 2015	152	2015
Logical hidden markov models K Kersting, L De Raedt, T Raiko Journal of Artificial Intelligence Research 25, 425-456, 2006	125	2006
Plant phenotyping using probabilistic topic models: uncovering the hyperspectral language of plants M Wahabzada, AK Mahlein, C Bauckhage, U Steiner, EC Oerke, ... Scientific reports 6 (1), 1-11, 2016	116	2016

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