OpenAI Gym G Brockman, V Cheung, L Pettersson, J Schneider, J Schulman, J Tang, ... arXiv preprint arXiv:1606.01540, 2016 | 7933 | 2016 |
Domain randomization for transferring deep neural networks from simulation to the real world J Tobin, R Fong, A Ray, J Schneider, W Zaremba, P Abbeel 2017 IEEE/RSJ international conference on intelligent robots and systems …, 2017 | 3365 | 2017 |
Hindsight Experience Replay M Andrychowicz, F Wolski, A Ray, J Schneider, R Fong, P Welinder, ... Advances in Neural Information Processing Systems, 5053-5063, 2017 | 2889 | 2017 |
Dota 2 with large scale deep reinforcement learning C Berner, G Brockman, B Chan, V Cheung, P Dębiak, C Dennison, ... arXiv preprint arXiv:1912.06680, 2019 | 1906 | 2019 |
Learning dexterous in-hand manipulation OAIM Andrychowicz, B Baker, M Chociej, R Jozefowicz, B McGrew, ... The International Journal of Robotics Research 39 (1), 3-20, 2020 | 1765 | 2020 |
Solving rubik's cube with a robot hand I Akkaya, M Andrychowicz, M Chociej, M Litwin, B McGrew, A Petron, ... arXiv preprint arXiv:1910.07113, 2019 | 1170 | 2019 |
One-shot imitation learning Y Duan, M Andrychowicz, B Stadie, OAI Jonathan Ho, J Schneider, ... Advances in neural information processing systems 30, 2017 | 802 | 2017 |
Multi-goal reinforcement learning: Challenging robotics environments and request for research M Plappert, M Andrychowicz, A Ray, B McGrew, B Baker, G Powell, ... arXiv preprint arXiv:1802.09464, 2018 | 569 | 2018 |
Openai gym. arXiv G Brockman, V Cheung, L Pettersson, J Schneider, J Schulman, J Tang, ... arXiv preprint arXiv:1606.01540 10, 2016 | 277 | 2016 |
Transfer from simulation to real world through learning deep inverse dynamics model P Christiano, Z Shah, I Mordatch, J Schneider, T Blackwell, J Tobin, ... arXiv preprint arXiv:1610.03518, 2016 | 268 | 2016 |
Openai gym. arXiv 2016 G Brockman, V Cheung, L Pettersson, J Schneider, J Schulman, J Tang, ... arXiv preprint arXiv:1606.01540, 2020 | 201 | 2020 |
Domain randomization and generative models for robotic grasping J Tobin, L Biewald, R Duan, M Andrychowicz, A Handa, V Kumar, ... 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2018 | 183 | 2018 |
Dota 2 with large scale deep reinforcement learning CB OpenAI, G Brockman, B Chan, V Cheung, P Debiak, C Dennison, ... arXiv preprint arXiv:1912.06680 2, 2019 | 115 | 2019 |
Parametric and multivariate uncertainty calibration for regression and object detection F Küppers, J Schneider, A Haselhoff European Conference on Computer Vision, 426-442, 2022 | 15 | 2022 |
Confidence calibration for object detection and segmentation F Küppers, A Haselhoff, J Kronenberger, J Schneider Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty …, 2022 | 11 | 2022 |
Bayesian confidence calibration for epistemic uncertainty modelling F Küppers, J Kronenberger, J Schneider, A Haselhoff 2021 IEEE Intelligent Vehicles Symposium (IV), 466-472, 2021 | 11 | 2021 |
Towards black-box explainability with Gaussian discriminant knowledge distillation A Haselhoff, J Kronenberger, F Kuppers, J Schneider Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 8 | 2021 |
Gravitational closure of weakly birefringent electrodynamics J Schneider, FP Schuller, N Stritzelberger, F Wolz arXiv preprint arXiv:1708.03870, 2017 | 8 | 2017 |
On feature relevance uncertainty: a Monte Carlo dropout sampling approach K Fabi, J Schneider arXiv preprint arXiv:2008.01468, 2020 | 7 | 2020 |
Modifications to the Etherington Distance Duality Relation and Observational Limits S More, H Niikura, J Schneider, FP Schuller, MC Werner arXiv preprint arXiv:1612.08784, 2016 | 5 | 2016 |