Robert Peharz

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i10-index	32	30

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Franz PernkopfGraz University of TechnologyVerified email at tugraz.at
Kristian KerstingProfessor of AI & ML, TU Darmstadt, Co-Director hessian.ai, DFKI, Germany, CLAIRE & ELLISVerified email at cs.tu-darmstadt.de
Antonio VergariLecturer, University of Edinburgh, UKVerified email at ed.ac.uk
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
Karl StelznerTU DarmstadtVerified email at cs.tu-darmstadt.de
Alejandro MolinaAmazonVerified email at amazon.com
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
Xiaoting ShaoTU DarmstadtVerified email at cs.tu-darmstadt.de
Florian B. PokornyMedical University of Graz, Austria & University of Augsburg, GermanyVerified email at medunigraz.at
Nicola Di MauroAssociate Professor of Computer Science, University of BariVerified email at uniba.it
Robert GensVerified email at cs.washington.edu
Steven LangPhD Student, Technical University of DarmstadtVerified email at cs.tu-darmstadt.de
Pranav SubramaniGraduate Student, University Of WaterlooVerified email at uwaterloo.ca
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Alvaro H.C. CorreiaEindhoven University of TechnologyVerified email at tue.nl
Thomas LiebigTU DortmundVerified email at tu-dortmund.de
Bernhard C. GeigerSenior Researcher at Know-Center GmbHVerified email at ieee.org

Robert Peharz

Assistant Professor of AI, TU Graz

Verified email at tugraz.at - Homepage

machine learning signal processing computer vision


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	179	2012
Sparse nonnegative matrix factorization using ℓ0-constraints R Peharz, M Stark, F Pernkopf Machine Learning for Signal Processing (MLSP), 2010 IEEE International …, 2010	179*	2010
Fidgety movements–tiny in appearance, but huge in impact C Einspieler, R Peharz, PB Marschik Jornal de Pediatria 92, 64-70, 2016	129	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	111	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	111	2015
Greedy part-wise learning of sum-product networks R Peharz, BC Geiger, F Pernkopf Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013	81	2013
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	80	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	80	2017
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	77	2014
Learning selective sum-product networks R Peharz, R Gens, P Domingos LTPM workshop 32, 2014	64	2014
Foundations of Sum-Product Networks for Probabilistic Modeling R Peharz Graz University of Technology, SPSC, 2015	63	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	61	2020
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	46	2019
Minimal random code learning: Getting bits back from compressed model parameters M Havasi, R Peharz, JM Hernández-Lobato arXiv preprint arXiv:1810.00440, 2018	43	2018
Faster attend-infer-repeat with tractable probabilistic models K Stelzner, R Peharz, K Kersting International Conference on Machine Learning, 5966-5975, 2019	42	2019
Introduction to probabilistic graphical models F Pernkopf, R Peharz, S Tschiatschek Academic Press Library in Signal Processing 1, 989-1064, 2014	36	2014
Resource-efficient neural networks for embedded systems W Roth, G Schindler, M Zöhrer, L Pfeifenberger, R Peharz, S Tschiatschek, ... arXiv preprint arXiv:2001.03048, 2020	35	2020
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	34	2020
Probabilistic deep learning using random sum-product networks R Peharz, A Vergari, K Stelzner, A Molina, M Trapp, K Kersting, ... arXiv preprint arXiv:1806.01910, 2018	34	2018
Automatic Bayesian density analysis A Vergari, A Molina, R Peharz, Z Ghahramani, K Kersting, I Valera Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 5207-5215, 2019	32	2019

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