Robert Peharz

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Coauteurs

Franz PernkopfGraz University of TechnologyAdresse e-mail validée de tugraz.at
Kristian KerstingProfessor of AI & Machine Learning, TU Darmstadt, Co-Director of hessian.ai, Germany, CLAIRE & ELLISAdresse e-mail validée de cs.tu-darmstadt.de
Martin TrappMachine learning researcher, Aalto UniversityAdresse e-mail validée de aalto.fi
Antonio VergariLecturer, University of Edinburgh, UKAdresse e-mail validée de ed.ac.uk
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleAdresse e-mail validée de eng.cam.ac.uk
Alejandro MolinaTU DarmstadtAdresse e-mail validée de cs.tu-darmstadt.de
Karl StelznerTU DarmstadtAdresse e-mail validée de cs.tu-darmstadt.de
Pedro DomingosProfessor of Computer Science and Engineering, University of WashingtonAdresse e-mail validée de cs.washington.edu
Peter B MarschikGeorg-August University Göttingen, Germany & Karolinska Institutet Stockholm, Sweden & MedicalAdresse e-mail validée de medunigraz.at
Robert GensAdresse e-mail validée de cs.washington.edu
Xiaoting ShaoTU DarmstadtAdresse e-mail validée de cs.tu-darmstadt.de
Nicola Di MauroAssociate Professor of Computer Science, University of BariAdresse e-mail validée de uniba.it
Florian B. PokornyMedical University of Graz, Austria & University of Augsburg, GermanyAdresse e-mail validée de medunigraz.at
Bernhard C. GeigerSenior Researcher at Know-Center GmbHAdresse e-mail validée de ieee.org
José Miguel Hernández-LobatoAssociate Professor in Machine Learning, University of CambridgeAdresse e-mail validée de cam.ac.uk
Pranav SubramaniGraduate Student, University Of WaterlooAdresse e-mail validée de uwaterloo.ca
Guy Van den BroeckAssociate Professor and Samueli Fellow, UCLAAdresse e-mail validée de cs.ucla.edu
Steven LangPhD Student, Technical University of DarmstadtAdresse e-mail validée de cs.tu-darmstadt.de
Pejman MowlaeeLead Research ScientistAdresse e-mail validée de tugraz.at
Thomas LiebigTU DortmundAdresse e-mail validée de tu-dortmund.de

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Robert Peharz

Assistant Professor of AI, TU Graz

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

machine learning signal processing computer vision


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	168	2012
Sparse nonnegative matrix factorization using ℓ0-constraints R Peharz, M Stark, F Pernkopf Machine Learning for Signal Processing (MLSP), 2010 IEEE International …, 2010	168*	2010
Fidgety movements–tiny in appearance, but huge in impact C Einspieler, R Peharz, PB Marschik Jornal de Pediatria 92, 64-70, 2016	108	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	105	2015
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	97	2016
Greedy part-wise learning of sum-product networks R Peharz, BC Geiger, F Pernkopf Joint European Conference on Machine Learning and Knowledge Discovery in …, 2013	78	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	73	2014
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 (5), 1-15, 2017	68	2017
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	59	2020
Foundations of Sum-Product Networks for Probabilistic Modeling R Peharz Graz University of Technology, SPSC, 2015	59	2015
Learning selective sum-product networks R Peharz, R Gens, P Domingos LTPM workshop 32, 2014	58	2014
Faster attend-infer-repeat with tractable probabilistic models K Stelzner, R Peharz, K Kersting International Conference on Machine Learning, 5966-5975, 2019	35	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	34	2018
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	33	2019
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	33	2018
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	32	2020
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	31	2020
Introduction to probabilistic graphical models F Pernkopf, R Peharz, S Tschiatschek Academic Press Library in Signal Processing 1, 989-1064, 2014	31	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	25	2020
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	25	2019

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