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Jonas Schneider
Jonas Schneider
OpenAI
Verified email at jonasschneider.com - Homepage
Title
Cited by
Cited by
Year
OpenAI Gym
G Brockman, V Cheung, L Pettersson, J Schneider, J Schulman, J Tang, ...
arXiv preprint arXiv:1606.01540, 2016
82572016
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
35162017
Hindsight Experience Replay
M Andrychowicz, F Wolski, A Ray, J Schneider, R Fong, P Welinder, ...
Advances in Neural Information Processing Systems, 5053-5063, 2017
30132017
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
20232019
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
18482020
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
12332019
One-shot imitation learning
Y Duan, M Andrychowicz, B Stadie, OAI Jonathan Ho, J Schneider, ...
Advances in neural information processing systems 30, 2017
8222017
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
6012018
Openai gym. arXiv
G Brockman, V Cheung, L Pettersson, J Schneider, J Schulman, J Tang, ...
arXiv preprint arXiv:1606.01540 10, 2016
2912016
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
2712016
Openai gym. arXiv 2016
G Brockman, V Cheung, L Pettersson, J Schneider, J Schulman, J Tang, ...
arXiv preprint arXiv:1606.01540, 2020
2112020
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
1872018
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
1212019
Dota 2 with large scale deep reinforcement learning. arXiv 2019
C Berner, G Brockman, B Chan, V Cheung, P Debiak, C Dennison, ...
arXiv preprint arXiv:1912.06680, 0
53
Parametric and multivariate uncertainty calibration for regression and object detection
F Küppers, J Schneider, A Haselhoff
European Conference on Computer Vision, 426-442, 2022
172022
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
132022
Bayesian confidence calibration for epistemic uncertainty modelling
F Küppers, J Kronenberger, J Schneider, A Haselhoff
2021 IEEE Intelligent Vehicles Symposium (IV), 466-472, 2021
112021
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
82021
On feature relevance uncertainty: a Monte Carlo dropout sampling approach
K Fabi, J Schneider
arXiv preprint arXiv:2008.01468, 2020
82020
Gravitational closure of weakly birefringent electrodynamics
J Schneider, FP Schuller, N Stritzelberger, F Wolz
arXiv preprint arXiv:1708.03870, 2017
82017
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