Built on

J. Denker, D. Schwartz, B. Wittner, S. Solla, R. Howard, L. Jackel, and J. Hopfield, “Large automatic learning, rule extraction, and generalization,” Complex systems
1987
Earlier work this paper cites.
N. Tishby, E. Levin, and S. A. Solla, “Consistent inference of probabilities in layered networks: Predictions and generalization,” in International Joint Conference on Neural Networks
1989
Earlier work this paper cites.
J. S. Denker and Y. Lecun, “Transforming neural-net output levels to probability distributions,” in Advances in neural information processing systems
1991
Earlier work this paper cites.
D. J. MacKay, “A practical bayesian framework for backpropagation networks,” Neural computation
1992
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J. Paisley, D. Blei, and M. Jordan, “Variational bayesian inference with stochastic search,” arXiv preprint arXiv:1206.6430
Original
2012
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Springer Science & Business Media, 2012
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D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114
Original
2013
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M. D. Hoffman, D. M. Blei, C. Wang, and J. Paisley, “Stochastic variational inference,” The Journal of Machine Learning Research
2013
Earlier work this paper cites.

Similar

A. Damianou and N. Lawrence, “Deep gaussian processes,” in Artificial Intelligence and Statistics
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2014
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N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting.,” Journal of machine learning research
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2015
Cited alongside, same era.
J. Tompson, R. Goroshin, A. Jain, Y. LeCun, and C. Bregler, “Efficient object localization using convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Then

H. Xu, Y. Gao, F. Yu, and T. Darrell, “End-to-end learning of driving models from large-scale video datasets,” arXiv preprint arXiv:1612.01079
Original
2016
Later among the works it cites.
Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in international conference on machine learning
2016
Later among the works it cites.
Y. Gal and Z. Ghahramani, “A theoretically grounded application of dropout in recurrent neural networks,” in Advances in neural information processing systems
2016
Later among the works it cites.
P. McClure and N. Kriegeskorte, “Representing inferential uncertainty in deep neural networks through sampling,” 2016
2016
Later among the works it cites.
A. Kendall and Y. Gal, “What uncertainties do we need in bayesian deep learning for computer vision?,” arXiv preprint arXiv:1703.04977

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2018

Spatial Uncertainty Sampling for End-to-End Control

Amini, Alexander, Soleimany, Ava, Karaman, Sertac et al.

Understand

End-to-end trained neural networks (NNs) are a compelling approach to autonomous vehicle control because of their ability to learn complex tasks without manual engineering of rule-based decisions.

However, challenging road conditions, ambiguous navigation situations, and safety considerations require reliable uncertainty estimation for the eventual adoption of full-scale autonomous vehicles.
Bayesian deep learning approaches provide a way to estimate uncertainty by approximating the posterior distribution of weights given a set of training data.
Dropout training in deep NNs approximates Bayesian inference in a deep Gaussian process and can thus be used to estimate model uncertainty.

Built on

J. Denker, D. Schwartz, B. Wittner, S. Solla, R. Howard, L. Jackel, and J. Hopfield, “Large automatic learning, rule extraction, and generalization,” Complex systems
1987
Earlier work this paper cites.
N. Tishby, E. Levin, and S. A. Solla, “Consistent inference of probabilities in layered networks: Predictions and generalization,” in International Joint Conference on Neural Networks
1989
Earlier work this paper cites.
J. S. Denker and Y. Lecun, “Transforming neural-net output levels to probability distributions,” in Advances in neural information processing systems
1991
Earlier work this paper cites.
D. J. MacKay, “A practical bayesian framework for backpropagation networks,” Neural computation
1992
Earlier work this paper cites.
J. Paisley, D. Blei, and M. Jordan, “Variational bayesian inference with stochastic search,” arXiv preprint arXiv:1206.6430
Original
2012
Earlier work this paper cites.
Springer Science & Business Media, 2012
R. M. Neal, Bayesian learning for neural networks · 2012
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114
Original
2013
Earlier work this paper cites.
M. D. Hoffman, D. M. Blei, C. Wang, and J. Paisley, “Stochastic variational inference,” The Journal of Machine Learning Research
2013
Earlier work this paper cites.

Similar

A. Damianou and N. Lawrence, “Deep gaussian processes,” in Artificial Intelligence and Statistics
2013
Cited alongside, same era.
D. J. Rezende, S. Mohamed, and D. Wierstra, “Stochastic backpropagation and approximate inference in deep generative models,” arXiv preprint arXiv:1401.4082
Original
2014
Cited alongside, same era.
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting.,” Journal of machine learning research
2014
Cited alongside, same era.
Y. Gal and Z. Ghahramani, “Bayesian convolutional neural networks with bernoulli approximate variational inference,” arXiv preprint arXiv:1506.02158
Original
2015
Cited alongside, same era.
J. Tompson, R. Goroshin, A. Jain, Y. LeCun, and C. Bregler, “Efficient object localization using convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Then

H. Xu, Y. Gao, F. Yu, and T. Darrell, “End-to-end learning of driving models from large-scale video datasets,” arXiv preprint arXiv:1612.01079
Original
2016
Later among the works it cites.
Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in international conference on machine learning
2016
Later among the works it cites.
Y. Gal and Z. Ghahramani, “A theoretically grounded application of dropout in recurrent neural networks,” in Advances in neural information processing systems
2016
Later among the works it cites.
P. McClure and N. Kriegeskorte, “Representing inferential uncertainty in deep neural networks through sampling,” 2016
2016
Later among the works it cites.
A. Kendall and Y. Gal, “What uncertainties do we need in bayesian deep learning for computer vision?,” arXiv preprint arXiv:1703.04977

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

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2015

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A. Gurghian, T. Koduri, S. V. Bailur, K. J. Carey, and V. N. Murali, “Deeplanes: End-to-end lane position estimation using deep neural networksa,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops

2016

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M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, et al

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2016

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2017

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C. Leibig, V. Allken, M. S. Ayhan, P. Berens, and S. Wahl, “Leveraging uncertainty information from deep neural networks for disease detection,” bioRxiv

2017

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F. Naser, D. Dorhout, S. Proulx, S. D. Pendleton, H. Andersen, W. Schwarting, L. Paull, J. Alonso-Mora, M. H. Ang, S. Karaman, et al

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2017

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