| 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 | 69 | 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 | 47 | 2020 |
| One million posts: A data set of german online discussions D Schabus, M Skowron, M Trapp Proceedings of the 40th International ACM SIGIR Conference on Research and …, 2017 | 41 | 2017 |
| 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 |
| Bayesian learning of sum-product networks M Trapp, R Peharz, H Ge, F Pernkopf, Z Ghahramani Advances in Neural Information Processing Systems (NeurIPS), 2019 | 26 | 2019 |
| Deep Structured Mixtures of Gaussian Processes M Trapp, R Peharz, F Pernkopf, CE Rasmussen International Conference on Artificial Intelligence and Statistics (AISTATS), 2020 | 20 | 2020 |
| Safe Semi-Supervised Learning of Sum-Product Networks M Trapp, T Madl, R Peharz, F Pernkopf, R Trappl Uncertainty in Artificial Intelligence (UAI), 2017 | 17 | 2017 |
| Structure inference in sum-product networks using infinite sum-product trees M Trapp, R Peharz, M Skowron, T Madl, F Pernkopf, R Trappl NIPS Workshop on Practical Bayesian Nonparametrics, 2016 | 14 | 2016 |
| Automatic identification of character types from film dialogs M Skowron, M Trapp, S Payr, R Trappl Applied Artificial Intelligence 30 (10), 942-973, 2016 | 11 | 2016 |
| AdvancedHMC. jl: A robust, modular and efficient implementation of advanced HMC algorithms K Xu, H Ge, W Tebbutt, M Tarek, M Trapp, Z Ghahramani Symposium on Advances in Approximate Bayesian Inference, 1-10, 2020 | 8 | 2020 |
| Learning deep mixtures of gaussian process experts using sum-product networks M Trapp, R Peharz, CE Rasmussen, F Pernkopf arXiv preprint arXiv:1809.04400, 2018 | 8 | 2018 |
| Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression Z Yu, M Zhu, M Trapp, A Skryagin, K Kersting Conference on Uncertainty in Artificial Intelligence (UAI), 2021 | 6 | 2021 |
| Sum-product-transform networks: Exploiting symmetries using invertible transformations T Pevný, V Smídl, M Trapp, O Poláček, T Oberhuber International Conference on Probabilistic Graphical Models, 341-352, 2020 | 6 | 2020 |
| Grounded word learning on a pepper robot M Hirschmanner, S Gross, B Krenn, F Neubarth, M Trapp, M Vincze Proceedings of the 18th International Conference on Intelligent Virtual …, 2018 | 6 | 2018 |
| Periodic Activation Functions Induce Stationarity L Meronen, M Trapp, A Solin Advances in Neural Information Processing Systems 34, 1673-1685, 2021 | 3 | 2021 |
| Graph tracking in dynamic probabilistic programs via source transformations P Gabler, M Trapp, H Ge, F Pernkopf | 3 | 2019 |
| Optimisation of overparametrized sum-product networks M Trapp, R Peharz, F Pernkopf arXiv preprint arXiv:1905.08196, 2019 | 3 | 2019 |
| 3D object retrieval in an atlas of neuronal structures M Trapp, F Schulze, K Bühler, T Liu, BJ Dickson The Visual Computer 29 (12), 1363-1373, 2013 | 3 | 2013 |
| Similarity Based Object Retrieval of Composite Neuronal Structures F Schulze, M Trapp, K Bühler, T Lui, B Dickson Eurographics 2012 Workshop on 3D Object Retrieval, 1-8, 2012 | 3 | 2012 |
| Deep residual mixture models P Hämäläinen, M Trapp, T Saloheimo, A Solin arXiv preprint arXiv:2006.12063, 2020 | 2 | 2020 |
Robert PeharzAssistant Professor of AI, TU GrazAdresse e-mail validée de tugraz.at
