Fetching the paper…
Reading the bibliography…
Cyber-physical systems (CPS) greatly benefit by using machine learning components that can handle the uncertainty and variability of the real-world.
M. Basseville, I. V. Nikiforov et al. , Detection of abrupt changes: theory and application . Prentice Hall Englewood Cliffs, 1993, vol. 104
1993
Earlier work this paper cites.
V. Vovk, A. Gammerman, and G. Shafer, Algorithmic learning in a random world . Springer Science & Business Media, 2005
2005
Earlier work this paper cites.
V. Fedorova, A. Gammerman, I. Nouretdinov, and V. Vovk, “Plug-in martingales for testing exchangeability on-line,” in 29th International Conference on Machine Learning , 2012, pp. 923–930
2012
Earlier work this paper cites.
R. Laxhammar and G. Falkman, “Online learning and sequential anomaly detection in trajectories,” IEEE transactions on pattern analysis and machine intelligence , vol. 36, no. 6, pp. 1158–1173, 2013
2013
Earlier work this paper cites.
J. Smith, I. Nouretdinov, R. Craddock, C. Offer, and A. Gammerman, “Anomaly detection of trajectories with kernel density estimation by conformal prediction,” in IFIP International Conference on Artificial Intelligence Applications and Innovations , 2014, pp. 271–280
2014
Earlier work this paper cites.
V. Balasubramanian, S.-S. Ho, and V. Vovk, Conformal prediction for reliable machine learning: theory, adaptations and applications . Newnes, 2014
2014
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in International Conference on Learning Representations , 2014
2014
Earlier work this paper cites.
——, “Inductive conformal anomaly detection for sequential detection of anomalous sub-trajectories,” Annals of Mathematics and Artificial Intelligence , vol. 74, no. 1-2, pp. 67–94, 2015
2015
Earlier work this paper cites.
J. An and S. Cho, “Variational autoencoder based anomaly detection using reconstruction probability,” Special Lecture on IE, (2)1 , 2015
2015
Earlier work this paper cites.
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” in International Conference on Learning Representations , 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
T. Dreossi, A. Donzé, and S. A. Seshia, “Compositional falsification of cyber-physical systems with machine learning components,” in NASA Formal Methods Symposium . Springer, 2017, pp. 357–372
2017
Cited alongside, same era.
D. Hendrycks and K. Gimpel, “A baseline for detecting misclassified and out-of-distribution examples in neural networks,” International Conference on Learning Representations , 2017
2017
Cited alongside, same era.
L. Ruff, R. Vandermeulen, N. Goernitz, L. Deecke, S. A. Siddiqui, A. Binder, E. Müller, and M. Kloft, “Deep one-class classification,” in International Conference on Machine Learning , 2018, pp. 4393–4402
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
C. Richter and N. Roy, “Safe visual navigation via deep learning and novelty detection,” in Robotics: Science and Systems , 2017
2017
Cited alongside, same era.
D. Volkhonskiy, E. Burnaev, I. Nouretdinov, A. Gammerman, and V. Vovk, “Inductive conformal martingales for change-point detection,” Proceedings of Machine Learning Research , vol. 60, pp. 1–22, 2017
2017
Cited alongside, same era.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An open urban driving simulator,” in Proceedings of the 1st Annual Conference on Robot Learning , 2017, pp. 1–16
2017
Cited alongside, same era.
S. Liang, Y. Li, and R. Srikant, “Enhancing the reliability of out-of-distribution image detection in neural networks,” in ICLR , 2017
2017
Cited alongside, same era.
C. E. Tuncali, G. Fainekos, H. Ito, and J. Kapinski, “Simulation-based adversarial test generation for autonomous vehicles with machine learning components,” in 2018 IEEE Intelligent Vehicles Symposium
2018
Cited alongside, same era.
D. Hendrycks, M. Mazeika, and T. Dietterich, “Deep anomaly detection with outlier exposure,” International Conference on Learning Representations , 2019
2019
Later among the works it cites.
R. McAllister, G. Kahn, J. Clune, and S. Levine, “Robustness to out-of-distribution inputs via task-aware generative uncertainty,” in International Conference on Robotics and Automation (ICRA) , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
X. Gu and A. Easwaran, “Towards safe machine learning for cps: infer uncertainty from training data,” in 10th ACM/IEEE International Conference on Cyber-Physical Systems , 2019
2019
Later among the works it cites.