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This work presents a novel ensemble of Bayesian Neural Networks (BNNs) for control of safety-critical systems.
R. Isermann, “Process fault detection based on modeling and estimation methods—a survey,” automatica , vol. 20, no. 4, pp. 387–404, 1984
1984
Earlier work this paper cites.
G. Stein, “Respect the Unstable,” IEEE Control Systems Magazine , 2003. [Online]. Available: https://ieeexplore.ieee.org/document/1213600/
2003
Earlier work this paper cites.
Y. Tassa, T. Erez, and W. D. Smart, “Receding horizon differential dynamic programming,” Advances in Neural Information Processing Systems 20 , pp. 1465–1472, 2008. [Online]. Available: http://papers.nips.cc/paper/3297-receding-horizon-differential-dynamic-programming.pdf
2008
Earlier work this paper cites.
S. J. Qin, “Data-driven fault detection and diagnosis for complex industrial processes,” IFAC Proceedings Volumes , vol. 42, no. 8, pp. 1115–1125, 2009
2009
Earlier work this paper cites.
A. Nguyen, J. Yosinski, and J. Clune, “Deep neural networks are easily fooled: High confidence predictions for unrecognizable images,” Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on , 2015. [Online]. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7298640
2015
Earlier work this paper cites.
I. Mordatch, K. Lowrey, and E. Todorov, “Ensemble-cio: Full-body dynamic motion planning that transfers to physical humanoids,” Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on , 2015. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7354126/
2015
Earlier work this paper cites.
S. Hussain, M. Mokhtar, and J. M. Howe, “Sensor failure detection, identification, and accommodation using fully connected cascade neural network,” IEEE Transactions on Industrial Electronics , vol. 62, no. 3, pp. 1683–1692, March 2015
2015
Earlier work this paper cites.
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra, “Weight uncertainty in neural network,” in Proceedings of the 32nd International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, F. Bach and D. Blei, Eds., vol. 37. Lille, France: PMLR, 07–09 Jul 2015, pp. 1613–1622. [Online]. Available: http://proceedings.mlr.press/v37/blundell15.html
2015
Earlier work this paper cites.
2015
Cited alongside, same era.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature , vol. 529, no. 7587, pp. 484–489, Jan 2016. [Online]. Available: http://www.nature.com/articles/nature16961
2016
Cited alongside, same era.
G. Litjens, C. I. Sánchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, C. Hulsbergen van de Kaa, P. Bult, B. van Ginneken, and J. van der Laak, “Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis,” Scientific Reports , 2016. [Online]. Available: https://doi.org/10.1038/srep26286
2016
Cited alongside, same era.
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
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S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,” Journal of Machine Learning Research , vol. 17, no. 39, pp. 1–40, 2016. [Online]. Available: http://jmlr.org/papers/v17/15-522.html
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in Proceedings of The 33rd International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, vol. 48. New York, New York, USA: PMLR, 20–22 Jun 2016, pp. 1050–1059. [Online]. Available: http://proceedings.mlr.press/v48/gal16.html
2016
Cited alongside, same era.
D. Shen, G. Wu, and H.-I. Suk, “Deep learning in medical image analysis,” Annual Review of Biomedical Engineering , vol. 19, no. 1, pp. 221–248, 2017, pMID: 28301734. [Online]. Available: https://doi.org/10.1146/annurev-bioeng-071516-044442
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Later among the works it cites.
Y. Pan, C.-A. Cheng, K. Saigol, K. Lee, X. Yan, E. A. Theodorou, and B. Boots, “Agile autonomous driving using end-to-end deep imitation learning,” Robotics: Science and Systems , 2018. [Online]. Available: http://www.roboticsproceedings.org/rss14/p56.pdf
2018
Closest in time.
K. Lee, K. Saigol, and E. A. Theodorou, “Early failure detection of deep end-to-end control policy by reinforcement learning,” 2019 IEEE International Conference on Robotics and Automation (ICRA) , 2019. [Online]. Available: https://ieeexplore.ieee.org/document/8794189
2019
Closest in time.