Kristian Kersting

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Sriraam NatarajanUniversity of Texas at DallasDirección de correo verificada de utdallas.edu
Luc De Raedtprofessor, KU Leuven & guestprofessor, Örebro UniversityDirección de correo verificada de cs.kuleuven.be
Christian BauckhageProf. of Computer Science, University of Bonn, Fraunhofer IAIS, LAMARR InstituteDirección de correo verificada de bit.uni-bonn.de
Alejandro MolinaAmazonDirección de correo verificada de amazon.com
Mirwaes WahabzadaBASF Digital Farming GmbHDirección de correo verificada de basf.com
Marion NeumannWashington University in St. LouisDirección de correo verificada de wustl.edu
Tushar KhotAllen Institute for AI; University of Wisconsin-MadisonDirección de correo verificada de allenai.org
Anne-Katrin MahleinInstitute of Sugar Beet Research, GöttingenDirección de correo verificada de ifz-goettingen.de
Fabian Hadijigoedle.io GmbHDirección de correo verificada de goedle.io
Jude ShavlikProfessor of Computer Science and of Biostatistics & Medical Informatics, University of WisconsinDirección de correo verificada de cs.wisc.edu
Christian ThurauN26Dirección de correo verificada de n26.com
Martin MladenovGoogle ResearchDirección de correo verificada de google.com
Babak AhmadiFraunhofer IAISDirección de correo verificada de iais.fraunhofer.de
Constantin A. RothkopfProfessor, Technical University Darmstadt & Adjunct Fellow, FIASDirección de correo verificada de cogsci.tu-darmstadt.de
Petra MutzelProfessorin für Informatik, Universität BonnDirección de correo verificada de cs.uni-bonn.de
Antonio VergariReader (Associate Professor), University of Edinburgh, UKDirección de correo verificada de ed.ac.uk
Wolfram BurgardProfessor of Computer Science, Technical University of NurembergDirección de correo verificada de utn.de
Robert PeharzAssistant Professor of AI, TU GrazDirección de correo verificada de tugraz.at
Christopher MorrisRWTH Aachen UniversityDirección de correo verificada de cs.rwth-aachen.de
Christian PlagemannGoogle ResearchDirección de correo verificada de google.com

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Kristian Kersting

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

Dirección de correo verificada de cs.tu-darmstadt.de - Página principal

Artificial Intelligence Neurosymbolic AI Probabilistic Circuits Interpretability Machine Learning


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
TUDataset: A collection of benchmark datasets for learning with graphs MN Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting ... ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020), 2020	1181*	2020
Probabilistic Inductive Logic Programming L De Raedt, K Kersting Probabilistic Inductive Logic Programming - Theory and Applications, 1-27, 2008	495	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	467	2007
Statistical relational artificial intelligence: Logic, probability, and computation L De Raedt, K Kersting, S Natarajan, D Poole Springer Nature, 2022	391*	2022
Adaptive Bayesian logic programs K Kersting, L De Raedt International Conference on Inductive Logic Programming, 104-117, 2001	383	2001
Propagation kernels: efficient graph kernels from propagated information M Neumann, R Garnett, C Bauckhage, K Kersting Machine learning 102, 209-245, 2016	296	2016
Large pre-trained language models contain human-like biases of what is right and wrong to do P Schramowski, C Turan, N Andersen, CA Rothkopf, K Kersting Nature Machine Intelligence 4 (3), 258-268, 2022	290	2022
DeepDB: Learn from Data, not from Queries! B Hilprecht, A Schmidt, M Kulessa, A Molina, K Kersting, C Binnig PVLDB 13 (7), 2020	286	2020
Explanatory Interactive Machine Learning S Teso, K Kersting Proceedings of the 2nd AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2019	249	2019
Lifted Probabilistic Inference with Counting Formulas. B Milch, LS Zettlemoyer, K Kersting, M Haimes, LP Kaelbling AAAI 8, 1062-1068, 2008	249	2008
Making deep neural networks right for the right scientific reasons by interacting with their explanations P Schramowski, W Stammer, S Teso, A Brugger, F Herbert, X Shao, ... Nature Machine Intelligence 2 (8), 476-486, 2020	243	2020
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	234	2014
Probabilistic logic learning L De Raedt, K Kersting ACM SIGKDD Explorations Newsletter 5 (1), 31-48, 2003	224	2003
Bayesian Logic Programming: Theory and Tool K Kersting, L De Raedt Introduction to Statistical Relational Learning, 291, 2007	223	2007
Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models P Schramowski, M Brack, B Deiseroth, K Kersting Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023	204	2023
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, 1-15, 2015	204	2015
Towards combining inductive logic programming with Bayesian networks K Kersting, L De Raedt International Conference on Inductive Logic Programming, 118-131, 2001	204	2001
Counting belief propagation K Kersting, B Ahmadi, S Natarajan arXiv preprint arXiv:1205.2637, 2012	199	2012
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, 25-56, 2012	193	2012
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	185	2012

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