Fetching the paper…
Reading the bibliography…
Giving autonomous agents the ability to forecast their own outcomes and uncertainty will allow them to communicate their competencies and be used more safely.
D. J. MacKay, “Bayesian methods for adaptive models,” 1992
1992
Earlier work this paper cites.
D. J. C. MacKay, “Probable networks and plausible predictions – a review of practical Bayesian methods for supervised neural networks,” Network: computation in neural systems , 1995
1995
Earlier work this paper cites.
R. M. Neal, Bayesian Learning for Neural Networks . Berlin, Heidelberg: Springer-Verlag, 1996
1996
Earlier work this paper cites.
R. Neuneier and O. Mihatsch, “Risk sensitive reinforcement learning,” Conference on Neural Information Processing Systems (NIPS) , vol. 11, 1998
1998
Earlier work this paper cites.
J. D. Lee and K. A. See, “Trust in automation: Designing for appropriate reliance,” Human Factors , 2004
2004
Earlier work this paper cites.
S. Ososky, D. Schuster, E. Phillips, and F. Jentsch, “Building appropriate trust in human-robot teams,” AAAI Spring Symposium - Technical Report , 2013
2013
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-Encoding Variational Bayes,” in International Conference on Learning Representations (ICLR) , 2014
2014
Earlier work this paper cites.
K. Sohn, H. Lee, and X. Yan, “Learning structured output representation using deep conditional generative models,” Conference on Neural Information Processing Systems (NIPS) , 2015
2015
Earlier work this paper cites.
M. Aitken, N. R. Ahmed, D. A. Lawrence, B. Argrow, and E. W. Frew, “Assurances and machine self-confidence for enhanced trust in autonomous systems,” Robotics: Science and Systems (RSS) Workshop on Social Trust in Autonomous Systems , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
I. Osband, “Risk versus uncertainty in deep learning: Bayes, bootstrap and the dangers of dropout,” in Conference on Neural Information Processing Systems (NIPS) Workshop on Bayesian Deep Learning , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
A. Kendall and Y. Gal, “What uncertainties do we need in Bayesian deep learning for computer vision?” in Conference on Neural Information Processing Systems (NIPS) , 2017
2017
Earlier work this paper cites.
G. J. S. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Medical image analysis , 2017
2017
Cited alongside, same era.
H. A. Pierson and M. S. Gashler, “Deep learning in robotics: a review of recent research,” Advanced Robotics , 2017
2017
Cited alongside, same era.
B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles,” Conference on Neural Information Processing Systems (NIPS) , 2017
2017
Cited alongside, same era.
S. Depeweg, J. M. Hernández-Lobato, F. Doshi-Velez, and S. Udluft, “Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning,” 2017
2017
Cited alongside, same era.
D. Ha and J. Schmidhuber, “World models,” in Conference on Neural Information Processing Systems (NIPS) , 2018
B. Charpentier, D. Zügner, and S. Günnemann, “Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts,” Conference on Neural Information Processing Systems (NeurIPs) , 2020
2020
Later among the works it cites.
H. Eriksson and C. Dimitrakakis, “Epistemic risk-sensitive reinforcement learning,” European Symposium on Artificial Neural Networks (ESANN) , 2020
2020
Later among the works it cites.
B. Charpentier, O. Borchert, D. Zügner, S. Geisler, and S. Günnemann, “Natural posterior network: Deep Bayesian predictive uncertainty for exponential family distributions,” International Conference on Learning Representations (ICLR) , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
K. Chua, R. Calandra, R. McAllister, and S. Levine, “Deep reinforcement learning in a handful of trials using probabilistic dynamics models,” Conference on Neural Information Processing Systems (NIPS) , 2018
2018
Cited alongside, same era.
B. W. Israelsen and N. R. Ahmed, ““Dave…I can assure you …that it’s going to be all right …” A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships,” ACM Computing Surveys (CSUR) , 2019
2019
Cited alongside, same era.
N. R. Ke, A. Singh, A. Touati, A. Goyal, Y. Bengio, D. Parikh, and D. Batra, “Learning dynamics model in reinforcement learning by incorporating the long term future,” International Conference on Learning Representations (ICLR) , 2019
2019
Cited alongside, same era.
A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean, “A guide to deep learning in healthcare,” Nature medicine , 2019
2019
Cited alongside, same era.
A. Sedlmeier, T. Gabor, T. Phan, L. Belzner, and C. Linnhoff-Popien, “Uncertainty-based out-of-distribution detection in deep reinforcement learning,” International Symposium on Applied Artificial Intelligence (ISAAI) , 2019
2019
Cited alongside, same era.
W. R. Clements, B. Robaglia, B. V. Delft, R. B. Slaoui, and S. Toth, “Estimating risk and uncertainty in deep reinforcement learning,” International Conference on Machine Learning (ICML) Workshop on Uncertainty and Robustness in Deep Learning , 2020
2020
Cited alongside, same era.
S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu, “A survey of deep learning techniques for autonomous driving,” Journal of Field Robotics , 2020
2020
Cited alongside, same era.
2021
Later among the works it cites.
Y. Wu, S. Zhai, N. Srivastava, J. Susskind, J. Zhang, R. Salakhutdinov, and H. Goh, “Uncertainty weighted actor-critic for offline reinforcement learning,” International Conference on Machine Learning (ICML) , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
M. Rigter, B. Lacerda, and N. Hawes, “Risk-averse Bayes-adaptive reinforcement learning,” Conference on Neural Information Processing Systems (NeurIPs) , 2021
2021
Later among the works it cites.
A. Acharya, R. Russell, and N. R. Ahmed, “Competency assessment for autonomous agents using deep generative models,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022
2022
Later among the works it cites.
B. Ivanovic, Y. Lin, S. Shrivastava, P. Chakravarty, and M. Pavone, “Propagating state uncertainty through trajectory forecasting,” in IEEE International Conference on Robotics and Automation (ICRA) , 2022
2022
Later among the works it cites.
A. Acharya, R. Russell, and N. R. Ahmed, “Uncertainty quantification for competency assessment of autonomous agents,” IEEE International Conference on Robotics and Automation (ICRA) Workshop on Safe and Reliable Robot Autonomy under Uncertainty , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
N. Conlon, A. Acharya, J. McGinley, T. Slack, C. A. Hirst, M. D’Alonzo, M. R. Hebert, C. Reale, E. W. Frew, R. Russell et al. , “Generalizing competency self-assessment for autonomous vehicles using deep reinforcement learning,” in American Institute of Aeronautics and Astronautics (AIAA) SciTech 2022 Forum , 2022
2022
Later among the works it cites.