| Sparse nonnegative matrix factorization using ℓ0-constraints R Peharz, M Stark, F Pernkopf Machine Learning for Signal Processing (MLSP), 2010 IEEE International …, 2010 | 207* | 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 | 173 | 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 | 133 | 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 | 130 | 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 | 123 | 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 | 117 | 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 | 94 | 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 | 90 | 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 | 84 | 2014 |
| Minimal random code learning: Getting bits back from compressed model parameters M Havasi, R Peharz, JM Hernández-Lobato | 73 | 2019 |
| Learning selective sum-product networks R Peharz, R Gens, P Domingos 31st International Conference on Machine Learning (ICML2014), 2014 | 73 | 2014 |
| Foundations of Sum-Product Networks for Probabilistic Modeling R Peharz Graz University of Technology, SPSC, 2015 | 68 | 2015 |
| 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 | 62 | 2021 |
| Faster attend-infer-repeat with tractable probabilistic models K Stelzner, R Peharz, K Kersting International Conference on Machine Learning, 5966-5975, 2019 | 59 | 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 | 58 | 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 | 51 | 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 | 48 | 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 | 46 | 2020 |
| Introduction to probabilistic graphical models F Pernkopf, R Peharz, S Tschiatschek Academic Press Library in Signal Processing 1, 989-1064, 2014 | 45 | 2014 |
| 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 | 42 | 2019 |
Franz PernkopfGraz University of Technology在 tugraz.at 的电子邮件经过验证
