Robert Peharz

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Franz PernkopfGraz University of Technology確認したメールアドレス: tugraz.at
Antonio VergariReader (Associate Professor), University of Edinburgh, UK確認したメールアドレス: ed.ac.uk
Kristian KerstingProfessor of AI & ML, Technical University of Darmstadt, Co-Director hessian.ai, DFKI, CAIRNE, ELLIS確認したメールアドレス: cs.tu-darmstadt.de
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, Google確認したメールアドレス: eng.cam.ac.uk
Martin TrappMachine learning researcher, Aalto University確認したメールアドレス: aalto.fi
Alejandro MolinaAmazon確認したメールアドレス: amazon.com
Karl StelznerTU Darmstadt確認したメールアドレス: cs.tu-darmstadt.de
Sebastian TschiatschekUniversity of Vienna確認したメールアドレス: univie.ac.at
Peter B MarschikGeorg-August University Göttingen, Germany & Karolinska Institutet Stockholm, Sweden & Medical確認したメールアドレス: medunigraz.at
Pedro DomingosProfessor of Computer Science and Engineering, University of Washington確認したメールアドレス: cs.washington.edu
Nicola Di MauroAssociate Professor of Computer Science, University of Bari確認したメールアドレス: uniba.it
Xiaoting ShaoTU Darmstadt確認したメールアドレス: cs.tu-darmstadt.de
Florian B. PokornyMedical University of Graz, Austria & Technical University of Munich, Germany確認したメールアドレス: medunigraz.at
Robert Gens確認したメールアドレス: cs.washington.edu
Alvaro H.C. CorreiaQualcomm AI Research確認したメールアドレス: qti.qualcomm.com
Matthias ZoehrerCTO Flapjack GmbH確認したメールアドレス: flapjack-sdk.com
Gennaro GalaEindhoven University of Technology確認したメールアドレス: tue.nl
Carl Edward RasmussenProfessor of Machine Learning, University of Cambridge確認したメールアドレス: cam.ac.uk
José Miguel Hernández-LobatoProfessor of Machine Learning, University of Cambridge確認したメールアドレス: cam.ac.uk
Thomas LiebigTU Dortmund確認したメールアドレス: tu-dortmund.de

フォロー

Robert Peharz

Assistant Professor of AI, TU Graz

確認したメールアドレス: tugraz.at - ホームページ

probabilistic machine learning probabilistic circuits AI for science


タイトル引用回数順公開年順タイトル順	引用先引用先	年
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	193	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
Minimal random code learning: Getting bits back from compressed model parameters M Havasi, R Peharz, JM Hernández-Lobato arXiv preprint arXiv:1810.00440, 2018	88	2018
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
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	81	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
Joints in random forests A Correia, R Peharz, CP de Campos Advances in neural information processing systems 33, 11404-11415, 2020	54	2020
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
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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