| Uncertainty-driven imagination for continuous deep reinforcement learning G Kalweit, J Boedecker Conference on robot learning, 195-206, 2017 | 141 | 2017 |
| Dynamic input for deep reinforcement learning in autonomous driving M Huegle, G Kalweit, B Mirchevska, M Werling, J Boedecker 2019 IEEE/RSJ international conference on intelligent robots and systems …, 2019 | 68 | 2019 |
| Latent plans for task-agnostic offline reinforcement learning E Rosete-Beas, O Mees, G Kalweit, J Boedecker, W Burgard Conference on Robot Learning, 1838-1849, 2023 | 41 | 2023 |
| Dynamic interaction-aware scene understanding for reinforcement learning in autonomous driving M Hügle, G Kalweit, M Werling, J Boedecker 2020 IEEE international conference on robotics and automation (ICRA), 4329-4335, 2020 | 39 | 2020 |
| Adversarial skill networks: Unsupervised robot skill learning from video O Mees, M Merklinger, G Kalweit, W Burgard 2020 IEEE International Conference on Robotics and Automation (ICRA), 4188-4194, 2020 | 29 | 2020 |
| Deep inverse q-learning with constraints G Kalweit, M Huegle, M Werling, J Boedecker Advances in Neural Information Processing Systems 33, 14291-14302, 2020 | 29 | 2020 |
| Personalized prediction of disease activity in patients with rheumatoid arthritis using an adaptive deep neural network M Kalweit, UA Walker, A Finckh, R Müller, G Kalweit, A Scherer, ... PLoS One 16 (6), e0252289, 2021 | 27 | 2021 |
| Q-learning with long-term action-space shaping to model complex behavior for autonomous lane changes G Kalweit, M Huegle, M Werling, J Boedecker 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 25* | 2021 |
| Affordance learning from play for sample-efficient policy learning J Borja-Diaz, O Mees, G Kalweit, L Hermann, J Boedecker, W Burgard 2022 International Conference on Robotics and Automation (ICRA), 6372-6378, 2022 | 24 | 2022 |
| A dynamic deep neural network for multimodal clinical data analysis M Hügle, G Kalweit, T Hügle, J Boedecker Explainable AI in Healthcare and Medicine: Building a Culture of …, 2021 | 18 | 2021 |
| Dorsal finger fold recognition by convolutional neural networks for the detection and monitoring of joint swelling in patients with rheumatoid arthritis T Hügle, L Caratsch, M Caorsi, J Maglione, D Dan, A Dumusc, ... Digital Biomarkers 6 (2), 31-35, 2022 | 16 | 2022 |
| Robust and Data-efficient Q-learning by Composite Value-estimation G Kalweit, M Kalweit, J Boedecker Transactions on Machine Learning Research, 2022 | 12* | 2022 |
| Amortized Q-learning with model-based action proposals for autonomous driving on highways B Mirchevska, M Hügle, G Kalweit, M Werling, J Boedecker 2021 IEEE international conference on robotics and automation (ICRA), 1028-1035, 2021 | 10 | 2021 |
| Deep surrogate Q-learning for autonomous driving M Kalweit, G Kalweit, M Werling, J Boedecker 2022 International Conference on Robotics and Automation (ICRA), 1578-1584, 2022 | 5 | 2022 |
| NeuRL: Closed-form Inverse Reinforcement Learning for Neural Decoding G Kalweit, M Kalweit, M Alyahyay, Z Jaeckel, F Steenbergen, S Hardung, ... arXiv preprint arXiv:2204.04733, 2022 | 5 | 2022 |
| Mechanisms of premotor-motor cortex interactions during goal directed behavior M Alyahyay, G Kalweit, M Kalweit, G Karvat, J Ammer, A Schneider, ... bioRxiv, 2023.01. 20.524944, 2023 | 2 | 2023 |
| AnyNets: Adaptive Deep Neural Networks for Medical Data with Missing Values. M Kalweit, G Kalweit, J Boedecker AI4Function@ IJCAI, 12-21, 2021 | 2 | 2021 |
| Künstliche Intelligenz in der Krebstherapie G Kalweit, M Kalweit, ID Mastroleo, J Bödecker, R Mertelsmann University of Freiburg, 2023 | 1 | 2023 |
| Training a Reinforcement Learning Agent to Control an Autonomous System J Boedecker, M Huegle, G Kalweit, M Werling US Patent App. 18/020,688, 2024 | | 2024 |
| AI as an Always-available Oncologist: A Vision for AI-optimized Cancer Therapy Based on Real-time Adaptive Dosing at the Patient Level G Kalweit, LG Valiña, I Mastroleo, A Klett, J Boedecker, R Mertelsmann, ... | | 2024 |
