| The value equivalence principle for model-based reinforcement learning C Grimm, A Barreto, S Singh, D Silver Advances in neural information processing systems 33, 5541-5552, 2020 | 80 | 2020 |
| Deep abstract q-networks M Roderick, C Grimm, S Tellex arXiv preprint arXiv:1710.00459, 2017 | 40 | 2017 |
| Proper value equivalence C Grimm, A Barreto, G Farquhar, D Silver, S Singh Advances in neural information processing systems 34, 7773-7786, 2021 | 33 | 2021 |
| Disentangled cumulants help successor representations transfer to new tasks C Grimm, I Higgins, A Barreto, D Teplyashin, M Wulfmeier, T Hertweck, ... arXiv preprint arXiv:1911.10866, 2019 | 18 | 2019 |
| Mitigating planner overfitting in model-based reinforcement learning D Arumugam, D Abel, K Asadi, N Gopalan, C Grimm, JK Lee, L Lehnert, ... arXiv preprint arXiv:1812.01129, 2018 | 13 | 2018 |
| Warping of radar data into camera image for cross-modal supervision in automotive applications C Grimm, T Fei, E Warsitz, R Farhoud, T Breddermann, R Haeb-Umbach IEEE Transactions on Vehicular Technology 71 (9), 9435-9449, 2022 | 12 | 2022 |
| An efficient sparse sensing based interference mitigation approach for automotive radar T Fei, H Guang, Y Sun, C Grimm, E Warsitz 2020 17th European Radar Conference (EuRAD), 274-277, 2021 | 10 | 2021 |
| Hypothesis test for the detection of moving targets in automotive radar C Grimm, T Breddermann, R Farhoud, T Fei, E Warsitz, R Haeb-Umbach 2017 IEEE International Conference on Microwaves, Antennas, Communications …, 2017 | 9 | 2017 |
| Discrimination of stationary from moving targets with recurrent neural networks in automotive radar C Grimm, T Breddermann, R Farhoud, T Fei, E Warsitz, R Haeb-Umbach 2018 IEEE MTT-S International Conference on Microwaves for Intelligent …, 2018 | 7 | 2018 |
| A novel target separation algorithm applied to the two-dimensional spectrum for FMCW automotive radar systems T Fei, C Grimm, R Farhoud, T Breddermann, E Warsitz, R Häb-Umbach 2017 IEEE International Conference on Microwaves, Antennas, Communications …, 2017 | 7 | 2017 |
| Detection of moving targets in automotive radar with distorted ego-velocity information C Grimm, R Farhoud, T Fei, E Warsitz, R Haeb-Umbach 2017 IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS), 111-116, 2017 | 7 | 2017 |
| Approximate value equivalence C Grimm, A Barreto, S Singh Advances in neural information processing systems 35, 33029-33040, 2022 | 6 | 2022 |
| Modeling latent attention within neural networks C Grimm, D Arumugam, S Karamcheti, D Abel, LLS Wong, ML Littman arXiv preprint arXiv:1706.00536, 2017 | 6 | 2017 |
| Learning independently-obtainable reward functions C Grimm, S Singh arXiv preprint arXiv:1901.08649, 2019 | 5 | 2019 |
| Latent attention networks C Grimm, D Arumugam, S Karamcheti, D Abel, LL Wong, ML Littman arXiv preprint arXiv:1706.00536, 2017 | 4 | 2017 |
| Weak and strong solutions to the inverse-square brachistochrone problem on circular and annular domains C Grimm, JA Gemmer Involve, a Journal of Mathematics 10 (5), 833-856, 2017 | 4 | 2017 |
| Neural network training on in-memory-computing hardware with radix-4 gradients C Grimm, N Verma IEEE Transactions on Circuits and Systems I: Regular Papers 69 (10), 4056-4068, 2022 | 3 | 2022 |
| Training Neural Networks With In-Memory-Computing Hardware and Multi-Level Radix-4 Inputs C Grimm, J Lee, N Verma IEEE Transactions on Circuits and Systems I: Regular Papers, 2024 | 1 | 2024 |
| Radar system for a vehicle MS Al Kadi, T Breddermann, R Farhoud, T Fei, C Grimm, E Warsitz US Patent 11,841,416, 2023 | 1 | 2023 |
| Learning approximate stochastic transition models Y Song, C Grimm, X Wang, ML Littman arXiv preprint arXiv:1710.09718, 2017 | 1 | 2017 |
