| 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 | 156* | 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 | 116 | 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 | 65 | 2017 |
| 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 | 52 | 2022 |
| Bayesian learning of sum-product networks M Trapp, R Peharz, H Ge, F Pernkopf, Z Ghahramani Advances in Neural Information Processing Systems (NeurIPS), 2019 | 51 | 2019 |
| Deep Structured Mixtures of Gaussian Processes M Trapp, R Peharz, F Pernkopf, CE Rasmussen International Conference on Artificial Intelligence and Statistics (AISTATS), 2020 | 46* | 2020 |
| 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 | 24 | 2020 |
| Periodic activation functions induce stationarity L Meronen, M Trapp, A Solin Advances in Neural Information Processing Systems 34, 1673-1685, 2021 | 18 | 2021 |
| Safe Semi-Supervised Learning of Sum-Product Networks M Trapp, T Madl, R Peharz, F Pernkopf, R Trappl Conference on Uncertainty in Artificial Intelligence (UAI), 2017 | 17 | 2017 |
| Automatic identification of character types from film dialogs M Skowron, M Trapp, S Payr, R Trappl Applied Artificial Intelligence 30 (10), 942-973, 2016 | 16 | 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 | 16 | 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 | 14 | 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 | 11 | 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 | 9 | 2018 |
| Fixing overconfidence in dynamic neural networks L Meronen, M Trapp, A Pilzer, L Yang, A Solin IEEE/CVF Winter Conference on Applications of Computer Vision, 2680-2690, 2024 | 7 | 2024 |
| Transport with Support: Data-Conditional Diffusion Bridges E Tamir, M Trapp, A Solin Transactions on Machine Learning Research (TMLR), 2023 | 7 | 2023 |
| 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 |
| Disentangling model multiplicity in deep learning A Heljakka, M Trapp, J Kannala, A Solin arXiv preprint arXiv:2206.08890, 2022 | 5* | 2022 |
| 3D object retrieval in an atlas of neuronal structures M Trapp, F Schulze, K Bühler, T Liu, BJ Dickson The Visual Computer 29, 1363-1373, 2013 | 5* | 2013 |
| 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 | 4 | 2022 |
Robert PeharzAssistant Professor of AI, TU Graz在 tugraz.at 的电子邮件经过验证
