| 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 | 136* | 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 | 93 | 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 | 54 | 2017 |
| Deep Structured Mixtures of Gaussian Processes M Trapp, R Peharz, F Pernkopf, CE Rasmussen International Conference on Artificial Intelligence and Statistics (AISTATS), 2020 | 43* | 2020 |
| Bayesian learning of sum-product networks M Trapp, R Peharz, H Ge, F Pernkopf, Z Ghahramani Advances in Neural Information Processing Systems (NeurIPS), 2019 | 43 | 2019 |
| Uncertainty-guided source-free domain adaptation S Roy, M Trapp, A Pilzer, J Kannala, N Sebe, E Ricci, A Solin European Conference on Computer Vision, 537-555, 2022 | 31 | 2022 |
| 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 | 17 | 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 |
| Periodic activation functions induce stationarity L Meronen, M Trapp, A Solin Advances in Neural Information Processing Systems 34, 1673-1685, 2021 | 15 | 2021 |
| Automatic identification of character types from film dialogs M Skowron, M Trapp, S Payr, R Trappl Applied Artificial Intelligence 30 (10), 942-973, 2016 | 15 | 2016 |
| 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 | 15 | 2016 |
| 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 | 12 | 2021 |
| 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 | 9 | 2018 |
| Sum-product-transform networks: Exploiting symmetries using invertible transformations T Pevnı, V Smídl, M Trapp, O Poláček, T Oberhuber International Conference on Probabilistic Graphical Models, 341-352, 2020 | 8 | 2020 |
| DynamicPPL: Stan-like speed for dynamic probabilistic models M Tarek, K Xu, M Trapp, H Ge, Z Ghahramani arXiv preprint arXiv:2002.02702, 2020 | 6 | 2020 |
| Transport with support: Data-conditional diffusion bridges E Tamir, M Trapp, A Solin arXiv preprint arXiv:2301.13636, 2023 | 5 | 2023 |
| Fixing Overconfidence in Dynamic Neural Networks L Meronen, M Trapp, A Pilzer, L Yang, A Solin arXiv preprint arXiv:2302.06359, 2023 | 4 | 2023 |
| Anomaly detection using generative models and sum-product networks in mammography scans M Dietrichstein, D Major, M Trapp, M Wimmer, D Lenis, P Winter, A Berg, ... MICCAI Workshop on Deep Generative Models, 77-86, 2022 | 3 | 2022 |
| Representational multiplicity should be exposed, not eliminated A Heljakka, M Trapp, J Kannala, A Solin arXiv preprint arXiv:2206.08890, 2022 | 3 | 2022 |
| Models of cross-situational and crossmodal word learning in task-oriented scenarios B Krenn, S Sadeghi, F Neubarth, S Gross, M Trapp, M Scheutz IEEE Transactions on Cognitive and Developmental Systems 12 (3), 658-668, 2020 | 3 | 2020 |
Robert PeharzAssistant Professor of AI, TU GrazVerified email at tugraz.at
