Martin Trapp
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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
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
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
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
Bayesian learning of sum-product networks
M Trapp, R Peharz, H Ge, F Pernkopf, Z Ghahramani
Advances in Neural Information Processing Systems (NeurIPS), 2019
Deep Structured Mixtures of Gaussian Processes
M Trapp, R Peharz, F Pernkopf, CE Rasmussen
International Conference on Artificial Intelligence and Statistics (AISTATS), 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
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
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
Automatic identification of character types from film dialogs
M Skowron, M Trapp, S Payr, R Trappl
Applied Artificial Intelligence 30 (10), 942-973, 2016
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
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
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
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
Periodic Activation Functions Induce Stationarity
L Meronen, M Trapp, A Solin
Advances in Neural Information Processing Systems 34, 1673-1685, 2021
Graph tracking in dynamic probabilistic programs via source transformations
P Gabler, M Trapp, H Ge, F Pernkopf
Optimisation of overparametrized sum-product networks
M Trapp, R Peharz, F Pernkopf
arXiv preprint arXiv:1905.08196, 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
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
Representational multiplicity should be exposed, not eliminated
A Heljakka, M Trapp, J Kannala, A Solin
arXiv preprint arXiv:2206.08890, 2022
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