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, TU Darmstadt, Co-Director hessian.ai, DFKI, Germany, CLAIRE & 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
Matthias ZoehrerCTO Flapjack GmbHVerified email at flapjack-sdk.com
Alvaro H.C. CorreiaQualcomm AI ResearchVerified email at qti.qualcomm.com
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Gennaro GalaEindhoven University of TechnologyVerified email at tue.nl
Thomas LiebigTU DortmundVerified email at tu-dortmund.de
Pranav SubramaniGraduate Student, University Of WaterlooVerified email at uwaterloo.ca

Robert Peharz

Assistant Professor of AI, TU Graz

Verified email at tugraz.at - Homepage

probabilistic machine learning probabilistic circuits AI for science


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Sparse nonnegative matrix factorization using ℓ0-constraints R Peharz, M Stark, F Pernkopf Machine Learning for Signal Processing (MLSP), 2010 IEEE International …, 2010	214*	2010
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	183	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	140	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	135	2015
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	127	2020
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	123	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	97	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	91	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	86	2014
Minimal random code learning: Getting bits back from compressed model parameters M Havasi, R Peharz, JM Hernández-Lobato	76	2019
Learning selective sum-product networks R Peharz, R Gens, P Domingos 31st International Conference on Machine Learning (ICML2014), 2014	74	2014
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	73	2021
Foundations of Sum-Product Networks for Probabilistic Modeling R Peharz Graz University of Technology, SPSC, 2015	69	2015
Faster attend-infer-repeat with tractable probabilistic models K Stelzner, R Peharz, K Kersting International Conference on Machine Learning, 5966-5975, 2019	63	2019
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	63	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	59*	2020
Bayesian learning of sum-product networks M Trapp, R Peharz, H Ge, F Pernkopf, Z Ghahramani Advances in neural information processing systems 32, 2019	53	2019
Introduction to probabilistic graphical models F Pernkopf, R Peharz, S Tschiatschek Academic Press Library in Signal Processing 1, 989-1064, 2014	48	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	47	2020
Joints in random forests A Correia, R Peharz, CP de Campos Advances in neural information processing systems 33, 11404-11415, 2020	44	2020

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