| A benchmark and simulator for uav tracking M Müller, N Smith, B Ghanem European conference on computer vision, 445-461, 2016 | 1705 | 2016 |
| DeepGCNs: Can GCNs Go as Deep as CNNs? G Li, M Müller, A Thabet, B Ghanem International Conference on Computer Vision (ICCV), 2019 | 1331 | 2019 |
| End-to-end driving via conditional imitation learning F Codevilla, M Müller, A López, V Koltun, A Dosovitskiy 2018 IEEE International Conference on Robotics and Automation (ICRA), 1-9, 2018 | 1146 | 2018 |
| TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild M Müller, A Bibi, S Giancola, S Al-Subaihi, B Ghanem European conference on computer vision, 2018 | 856 | 2018 |
| Context-aware correlation filter tracking M Müller, N Smith, B Ghanem Proc. of the IEEE Conference on Computer Vision and Pattern Recognition …, 2017 | 724 | 2017 |
| Driving Policy Transfer via Modularity and Abstraction M Müller, A Dosovitskiy, B Ghanem, V Koltun Conference on Robotic Learning (CoRL), 2018 | 238 | 2018 |
| Sim4CV: A photo-realistic simulator for computer vision applications M Müller, V Casser, J Lahoud, N Smith, B Ghanem International Journal of Computer Vision, 1-18, 2018 | 222* | 2018 |
| Sgas: Sequential greedy architecture search G Li, G Qian, IC Delgadillo, M Muller, A Thabet, B Ghanem Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 221 | 2020 |
| Training Graph Neural Networks with 1000 Layers G Li, M Müller, B Ghanem, V Koltun International Conference on Machine Learning (ICML), 2021 | 210 | 2021 |
| Learning high-speed flight in the wild A Loquercio, E Kaufmann, R Ranftl, M Müller, V Koltun, D Scaramuzza Science Robotics 6 (59), eabg5810, 2021 | 204 | 2021 |
| Target response adaptation for correlation filter tracking A Bibi, M Müller, B Ghanem European conference on computer vision, 419-433, 2016 | 189 | 2016 |
| Deepgcns: Making gcns go as deep as cnns G Li, M Müller, G Qian, IC Delgadillo, A Abualshour, A Thabet, B Ghanem IEEE transactions on pattern analysis and machine intelligence 45 (6), 6923-6939, 2021 | 171 | 2021 |
| Deep drone acrobatics E Kaufmann, A Loquercio, R Ranftl, M Müller, V Koltun, D Scaramuzza Robotics: Science and Systems (RSS), 2020 | 146 | 2020 |
| Champion-level drone racing using deep reinforcement learning E Kaufmann, L Bauersfeld, A Loquercio, M Müller, V Koltun, ... Nature 620 (7976), 982-987, 2023 | 127 | 2023 |
| Zoedepth: Zero-shot transfer by combining relative and metric depth SF Bhat, R Birkl, D Wofk, P Wonka, M Müller arXiv preprint arXiv:2302.12288, 2023 | 123 | 2023 |
| Persistent aerial tracking system for UAVs M Müller, G Sharma, N Smith, B Ghanem Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International …, 2016 | 65 | 2016 |
| OIL: Observational Imitation Learning G Li, M Müller, V Casser, N Smith, DL Michels, B Ghanem Robotics: Science and Systems (RSS), 2018 | 45* | 2018 |
| Reaching the limit in autonomous racing: Optimal control versus reinforcement learning Y Song, A Romero, M Müller, V Koltun, D Scaramuzza Science Robotics 8 (82), eadg1462, 2023 | 42 | 2023 |
| Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation M Müller, V Casser, N Smith, DL Michels, B Ghanem European conference on computer vision workshops, UAVision2018, 2017 | 38* | 2017 |
| SADA: semantic adversarial diagnostic attacks for autonomous applications A Hamdi, M Müller, B Ghanem Proceedings of the AAAI Conference on Artificial Intelligence 34 (07), 10901 …, 2020 | 29 | 2020 |
Bernard GhanemProfessor, King Abdullah University of Science and TechnologyDirección de correo verificada de kaust.edu.sa
