Robert Peharz

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Franz PernkopfGraz University of TechnologyVerified email at tugraz.at
Antonio VergariReader (Associate Professor), University of Edinburgh, UKVerified email at ed.ac.uk
Kristian KerstingProfessor of AI & ML, Technical University of Darmstadt, Co-Director hessian.ai, DFKI, CAIRNE, ELLISVerified email at cs.tu-darmstadt.de
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Martin TrappMachine learning researcher, Aalto UniversityVerified email at aalto.fi
Alejandro MolinaAmazonVerified email at amazon.com
Karl StelznerTU DarmstadtVerified email at cs.tu-darmstadt.de
Sebastian TschiatschekUniversity of ViennaVerified email at univie.ac.at
Peter B MarschikGeorg-August University Göttingen, Germany & Karolinska Institutet Stockholm, Sweden & MedicalVerified email at medunigraz.at
Pedro DomingosProfessor of Computer Science and Engineering, University of WashingtonVerified email at cs.washington.edu
Nicola Di MauroAssociate Professor of Computer Science, University of BariVerified email at uniba.it
Xiaoting ShaoTU DarmstadtVerified email at cs.tu-darmstadt.de
Florian B. PokornyMedical University of Graz, Austria & Technical University of Munich, GermanyVerified email at medunigraz.at
Robert GensVerified email at cs.washington.edu
Alvaro H.C. CorreiaQualcomm AI ResearchVerified email at qti.qualcomm.com
Matthias ZoehrerCTO Flapjack GmbHVerified email at flapjack-sdk.com
Gennaro GalaEindhoven University of TechnologyVerified email at tue.nl
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Thomas LiebigTU DortmundVerified email at tu-dortmund.de

Robert Peharz

Assistant Professor of AI, TU Graz

Verified email at tugraz.at - Homepage

probabilistic machine learning probabilistic circuits AI for science


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Sparse nonnegative matrix factorization with ℓ0-constraints R Peharz, F Pernkopf Neurocomputing 80, 38-46, 2012	220	2012
Fidgety movements–tiny in appearance, but huge in impact C Einspieler, R Peharz, PB Marschik Jornal de Pediatria 92 (3 Suppl 1), 64-70, 2016	192	2016
On the latent variable interpretation in sum-product networks R Peharz, R Gens, F Pernkopf, P Domingos IEEE transactions on pattern analysis and machine intelligence 39 (10), 2030 …, 2016	144	2016
On Theoretical Properties of Sum-Product Networks R Peharz, S Tschiatschek, F Pernkopf, P Domingos Proceedings of the Eighteenth International Conference on Artificial …, 2015	142	2015
Einsum networks: Fast and scalable learning of tractable probabilistic circuits R Peharz, S Lang, A Vergari, K Stelzner, A Molina, M Trapp, ... International Conference on Machine Learning, 7563-7574, 2020	134	2020
Random sum-product networks: A simple and effective approach to probabilistic deep learning R Peharz, A Vergari, K Stelzner, A Molina, X Shao, M Trapp, K Kersting, ... Uncertainty in Artificial Intelligence, 334-344, 2020	134	2020
A novel way to measure and predict development: A heuristic approach to facilitate the early detection of neurodevelopmental disorders PB Marschik, FB Pokorny, R Peharz, D Zhang, J O’Muircheartaigh, ... Current neurology and neuroscience reports 17, 1-15, 2017	108	2017
Greedy part-wise learning of sum-product networks R Peharz, BC Geiger, F Pernkopf Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013	92	2013
Modeling speech with sum-product networks: Application to bandwidth extension R Peharz, G Kapeller, P Mowlaee, F Pernkopf 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014	88	2014
Minimal random code learning: Getting bits back from compressed model parameters M Havasi, R Peharz, JM Hernández-Lobato arXiv preprint arXiv:1810.00440, 2018	86	2018
Novel AI driven approach to classify infant motor functions S Reich, D Zhang, T Kulvicius, S Bölte, K Nielsen-Saines, FB Pokorny, ... Scientific Reports 11 (1), 9888, 2021	80	2021
Learning selective sum-product networks R Peharz, R Gens, P Domingos 31st International Conference on Machine Learning (ICML2014), 2014	76	2014
Foundations of Sum-Product Networks for Probabilistic Modeling R Peharz Graz University of Technology, SPSC, 2015	71	2015
Spflow: An easy and extensible library for deep probabilistic learning using sum-product networks A Molina, A Vergari, K Stelzner, R Peharz, P Subramani, N Di Mauro, ... arXiv preprint arXiv:1901.03704, 2019	65	2019
Faster attend-infer-repeat with tractable probabilistic models K Stelzner, R Peharz, K Kersting International Conference on Machine Learning, 5966-5975, 2019	64	2019
Bayesian learning of sum-product networks M Trapp, R Peharz, H Ge, F Pernkopf, Z Ghahramani Advances in neural information processing systems 32, 2019	56	2019
Resource-efficient neural networks for embedded systems W Roth, G Schindler, B Klein, R Peharz, S Tschiatschek, H Fröning, ... arXiv preprint arXiv:2001.03048, 2020	53	2020
Joints in random forests A Correia, R Peharz, CP de Campos Advances in neural information processing systems 33, 11404-11415, 2020	53	2020
Introduction to probabilistic graphical models F Pernkopf, R Peharz, S Tschiatschek Academic Press Library in Signal Processing 1, 989-1064, 2014	50	2014
Conditional sum-product networks: Imposing structure on deep probabilistic architectures X Shao, A Molina, A Vergari, K Stelzner, R Peharz, T Liebig, K Kersting International Conference on Probabilistic Graphical Models, 401-412, 2020	49	2020

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