| Learning and policy search in stochastic dynamical systems with bayesian neural networks S Depeweg, JM Hernández-Lobato, F Doshi-Velez, S Udluft International Conference on Learning Representations (ICLR 2017), 2017 | 108 | 2017 |
| Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning S Depeweg, JM Hernández-Lobato, F Doshi-Velez, S Udluft ICML, 2018 | 92* | 2018 |
| A Benchmark Environment Motivated by Industrial Control Problems D Hein, S Depeweg, M Tokic, S Udluft, A Hentschel, TA Runkler, ... IEEE Symposium on Computational Intelligence (IEEE SSCI 2017), 2017 | 23 | 2017 |
| Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables S Depeweg, JM Hernández-Lobato, F Doshi-Velez, S Udluft ICML 2017 Workshop on Reliable Machine Learning in the Wild, 2017 | 22 | 2017 |
| Solving bongard problems with a visual language and pragmatic reasoning S Depeweg, CA Rothkopf, F Jäkel arXiv preprint arXiv:1804.04452, 2018 | 10 | 2018 |
| Modeling Epistemic and Aleatoric Uncertainty with Bayesian Neural Networks and Latent Variables S Depeweg PhD Thesis, 2019 | 5 | 2019 |
| Sensitivity Analysis for Predictive Uncertainty in Bayesian Neural Networks S Depeweg, JM Hernández-Lobato, S Udluft, T Runkler ESANN 2018, 2018 | 5 | 2018 |
| Learning navigation attractors for mobile robots with reinforcement learning and reservoir computing EA Antonelo, S Depeweg, B Schrauwen Proceedings of the X Brazilian Congress on Computational Intelligence (CBIC), 2011 | 2 | 2011 |
| A benchmark environment motivated by industrial control problems Download PDF D Hein, S Depeweg, M Tokic, S Udluft, A Hentschel, TA Runkler, ... | | |
