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There are two major types of uncertainty one can model.
Transforming neural-net output levels to probability distributions
John Denker and Yann LeCun · 1991
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A practical Bayesian framework for backpropagation networks
David JC MacKay · 1992
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A framework for spatiotemporal control in the tracking of visual contours
Andrew Blake, Rupert Curwen, and Andrew Zisserman · 1993
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Estimating the mean and variance of the target probability distribution
David A Nix and Andreas S Weigend · 1994
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Bayesian learning for neural networks
Radford M Neal · 1995
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An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
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Multiscale conditional random fields for image labeling
Xuming He, Richard S Zemel, and Miguel Á Carreira-Perpiñán · 2004
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Heteroscedastic Gaussian process regression
Quoc V Le, Alex J Smola, and Stéphane Canu · 2005
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Aleatory or epistemic? does it matter?
Armen Der Kiureghian and Ove Ditlevsen · 2009
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Semantic object classes in video: A high-definition ground truth database
Gabriel J Brostow, Julien Fauqueur, and Roberto Cipolla · 2009
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Make3d: Learning 3d scene structure from a single still image
Ashutosh Saxena, Min Sun, and Andrew Y Ng · 2009
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Practical variational inference for neural networks
Alex Graves · 2011
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Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
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Depth extraction from video using non-parametric sampling
Kevin Karsch, Ce Liu, and Sing Bing Kang · 2012
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Semantic image segmentation with deep convolutional nets and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2014
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Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Discrete-continuous depth estimation from a single image
Miaomiao Liu, Mathieu Salzmann, and Xuming He · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Uncertainty in Deep Learning
Y. Gal · 2016
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Black-box alpha divergence minimization
José Miguel Hernández-Lobato, Yingzhen Li, Daniel Hernández-Lobato, Thang Bui, and Richard E Turner · 2016
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Bayesian convolutional neural networks with Bernoulli approximate variational inference
Yarin Gal and Zoubin Ghahramani · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Kilian Q Weinberger, and Laurens van der Maaten · 2016
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The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation
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Lubor Ladicky, Jianbo Shi, and Marc Pollefeys · 2014
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Google photos labeled black people ’gorillas’
Jessica Guynn · 2015
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Alex Kendall, Vijay Badrinarayanan, and Roberto Cipolla · 2015
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Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2015
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
David Eigen and Rob Fergus · 2015
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Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs
Bo Li, Chunhua Shen, Yuchao Dai, Anton van den Hengel, and Mingyi He · 2015
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Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, and Yoshua Bengio · 2016
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Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
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Deeper depth prediction with fully convolutional residual networks
Iro Laina, Christian Rupprecht, Vasileios Belagiannis, Federico Tombari, and Nassir Navab · 2016
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Fully convolutional networks for semantic segmentation
Evan Shelhamer, Jonathon Long, and Trevor Darrell · 2016
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Feature space optimization for semantic video segmentation
Abhijit Kundu, Vibhav Vineet, and Vladlen Koltun · 2016
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PE 16-007
NHTSA · 2017
Closest in time.
SegNet: A deep convolutional encoder-decoder architecture for scene segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 2017
Closest in time.