Robert Peharz

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Franz PernkopfGraz University of TechnologyAdresse e-mail validée de tugraz.at
Antonio VergariReader (Associate Professor), University of Edinburgh, UKAdresse e-mail validée de ed.ac.uk
Kristian KerstingProfessor of AI & ML, Technical University of Darmstadt, Hessian.ai/DFKI, CAIRNE/ELLIS, AAAI FellowAdresse e-mail validée de cs.tu-darmstadt.de
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleAdresse e-mail validée de eng.cam.ac.uk
Martin TrappMachine learning researcher, Aalto UniversityAdresse e-mail validée de aalto.fi
Alejandro MolinaAmazonAdresse e-mail validée de amazon.com
Karl StelznerTU DarmstadtAdresse e-mail validée de cs.tu-darmstadt.de
Sebastian TschiatschekUniversity of ViennaAdresse e-mail validée de univie.ac.at
Peter B MarschikGeorg-August University Göttingen, Germany & Karolinska Institutet Stockholm, Sweden & MedicalAdresse e-mail validée de medunigraz.at
Pedro DomingosProfessor of Computer Science and Engineering, University of WashingtonAdresse e-mail validée de cs.washington.edu
Nicola Di MauroAssociate Professor of Computer Science, University of BariAdresse e-mail validée de uniba.it
Xiaoting ShaoTU DarmstadtAdresse e-mail validée de cs.tu-darmstadt.de
Florian B. PokornyMedical University of Graz, Austria & Technical University of Munich, GermanyAdresse e-mail validée de medunigraz.at
Robert GensAdresse e-mail validée de cs.washington.edu
Alvaro H.C. CorreiaQualcomm AI ResearchAdresse e-mail validée de qti.qualcomm.com
Matthias ZoehrerCTO Flapjack GmbHAdresse e-mail validée de flapjack-sdk.com
Gennaro GalaEindhoven University of TechnologyAdresse e-mail validée de tue.nl
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeAdresse e-mail validée de cam.ac.uk
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeAdresse e-mail validée de cam.ac.uk
Christian KnollLevata GmbHAdresse e-mail validée de ieee.org

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Robert Peharz

Assistant Professor of AI, TU Graz

Adresse e-mail validée de tugraz.at - Page d'accueil

probabilistic machine learning probabilistic circuits AI for science


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Sparse nonnegative matrix factorization with ℓ0-constraints R Peharz, F Pernkopf Neurocomputing 80, 38-46, 2012	222	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	196	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	146	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	145	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	137	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	137	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	110	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	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	85	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	83	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
Resource-Efficient Neural Networks for Embedded Systems W Roth, G Schindler, B Klein, R Peharz, S Tschiatschek, H Fröning, ... Journal of Machine Learning Research 25 (50), 1-51, 2024	70	2024
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	68	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	57	2019
Joints in random forests A Correia, R Peharz, CP de Campos Advances in neural information processing systems 33, 11404-11415, 2020	55	2020
Introduction to probabilistic graphical models F Pernkopf, R Peharz, S Tschiatschek Academic Press Library in signal processing 1, 989-1064, 2014	51	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	50	2020

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