| Normalizing kalman filters for multivariate time series analysis E de Bézenac, SS Rangapuram, K Benidis, M Bohlke-Schneider, R Kurle, ... Advances in Neural Information Processing Systems 33, 2995-3007, 2020 | 146 | 2020 |
| Metrics for deep generative models N Chen, A Klushyn, R Kurle, X Jiang, J Bayer, P Smagt International Conference on Artificial Intelligence and Statistics, 1540-1550, 2018 | 144 | 2018 |
| Learning hierarchical priors in vaes A Klushyn, N Chen, R Kurle, B Cseke, P van der Smagt Advances in neural information processing systems 32, 2019 | 113 | 2019 |
| Continual learning with bayesian neural networks for non-stationary data R Kurle, B Cseke, A Klushyn, P Van Der Smagt, S Günnemann International Conference on Learning Representations, 2019 | 95 | 2019 |
| I hear you eat and speak: Automatic recognition of eating condition and food type, use-cases, and impact on asr performance S Hantke, F Weninger, R Kurle, F Ringeval, A Batliner, AED Mousa, ... PloS one 11 (5), e0154486, 2016 | 48 | 2016 |
| Latent matters: Learning deep state-space models A Klushyn, R Kurle, M Soelch, B Cseke, P van der Smagt Advances in Neural Information Processing Systems 34, 10234-10245, 2021 | 43 | 2021 |
| Deep rao-blackwellised particle filters for time series forecasting R Kurle, SS Rangapuram, E de Bézenac, S Günnemann, J Gasthaus Advances in Neural Information Processing Systems 33, 15371-15382, 2020 | 42 | 2020 |
| Deep explicit duration switching models for time series AF Ansari, K Benidis, R Kurle, AC Turkmen, H Soh, AJ Smola, B Wang, ... Advances in Neural Information Processing Systems 34, 29949-29961, 2021 | 34 | 2021 |
| Multi-source neural variational inference R Kurle, S Günnemann, P Van der Smagt Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4114-4121, 2019 | 30 | 2019 |
| On the detrimental effect of invariances in the likelihood for variational inference R Kurle, R Herbrich, T Januschowski, Y Wang, J Gasthaus Advances in Neural Information Processing Systems 35, 2022 | 10 | 2022 |
| I hear you eat and speak: automatic recognition of eating condition and food type S Hantke, F Weninger, R Kurle, A Batliner, B Schuller to appear, 2015 | 10 | 2015 |
| On symmetries in variational Bayesian neural nets R Kurle, T Januschowski, J Gasthaus, YB Wang | 6 | 2021 |
| Intrinsic anomaly detection for multi-variate time series S Rabanser, T Januschowski, K Rasul, O Borchert, R Kurle, J Gasthaus, ... arXiv preprint arXiv:2206.14342, 2022 | 4 | 2022 |
| Metrics for deep generative models based on learned skills N Chen, A Klushyn, R Kurle, X Jiang, J Bayer, P van der Smagt Advances in Neural Information Processing Systems (NIPS) Workshop on …, 2017 | 2 | 2017 |
| xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference M Beck, K Pöppel, P Lippe, R Kurle, PM Blies, G Klambauer, S Böck, ... arXiv preprint arXiv:2503.13427, 2025 | | 2025 |
| BALI: Learning Neural Networks via Bayesian Layerwise Inference R Kurle, A Klushyn, R Herbrich arXiv preprint arXiv:2411.12102, 2024 | | 2024 |
| Variational Bayes for Continual Learning and Time-Series Forecasting R Kurle Technische Universität München, 2023 | | 2023 |
| Context-invariant, multi-variate time series representations S Rabanser, T Januschowski, K Rasul, O Borchert, R Kurle, J Gasthaus, ... | | |
Patrick van der SmagtELTE University | Foundation RoboticsVerified email at tum.de
