Fetching the paper…
Reading the bibliography…
With the widespread success of deep neural networks in science and technology, it is becoming increasingly important to quantify the uncertainty of the predictions produced by deep learning.
Mixture density networks
Bishop, C. M · 1994
Earlier work this paper cites.
An introduction to the bootstrap
Efron, B. and Tibshirani, R. J · 1994
Earlier work this paper cites.
Estimating the mean and variance of the target probability distribution
Nix, D. A. and Weigend, A. S · 1994
Earlier work this paper cites.
Practical confidence and prediction intervals
Heskes, T · 1997
Earlier work this paper cites.
Prediction intervals for artificial neural networks
Hwang, J. G. and Ding, A. A · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Bayesian goodness-of-fit testing using infinite-dimensional exponential families
Verdinelli, I., Wasserman, L., et al · 1998
Earlier work this paper cites.
Confidence and prediction intervals for neural network ensembles
Carney, J. G., Cunningham, P., and Bhagwan, U · 1999
Cited alongside, same era.
Consistency of bernstein polynomial posteriors
Petrone, S. and Wasserman, L · 2002
Cited alongside, same era.
A mixture approach to bayesian goodness of fit
Robert, C. and Rousseau, J · 2003
Cited alongside, same era.
Lower upper bound estimation method for construction of neural network-based prediction intervals
Khosravi, A., Nahavandi, S., Creighton, D., and Atiya, A. F · 2011
Cited alongside, same era.
Short-term load and wind power forecasting using neural network-based prediction intervals
Quan, H., Srinivasan, D., and Khosravi, A · 2014
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Later among the works it cites.
Deep reinforcement learning that matters
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., and Meger, D · 2017
Later among the works it cites.
What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A. and Gal, Y · 2017
Later among the works it cites.
Dropout inference in bayesian neural networks with alpha-divergences
Li, Y. and Gal, Y · 2017
Later among the works it cites.
Learning confidence for out-of-distribution detection in neural networks
DeVries, T. and Taylor, G. W · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Deep learning for identifying metastatic breast cancer
Wang, D., Khosla, A., Gargeya, R., Irshad, H., and Beck, A. H · 2016
Cited alongside, same era.
The evidence framework applied to classification networks
MacKay, D. J
Cited in the paper.
A practical bayesian framework for backpropagation networks
MacKay, D. J
Cited in the paper.
Later among the works it cites.
High-quality prediction intervals for deep learning: A distribution-free, ensembled approach
Pearce, T., Zaki, M., Brintrup, A., and Neely, A · 2018
Later among the works it cites.