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Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes.
A Practical Bayesian Framework for Backpropagation Networks
MacKay, D. J. C. (1992) · 1992
Earlier work this paper cites.
Novelty detection and neural network validation
Bishop, C. (1994) · 1994
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A Comparison of Some Error Estimates for Neural Network Models
Tibshirani, R. (1996) · 1996
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Computing with infinite networks
Williams, C. K. I. (1996) · 1996
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Bayesian Learning for Neural Networks
Neal, R. M. (1997) · 1997
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Dearden, R., Friedman, N., and Russell, S. (1998) · 1998
Earlier work this paper cites.
An Iterative Ensemble Kalman Filter for Multiphase Fluid Flow Data Assimilation
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Earlier work this paper cites.
The Matrix Cookbook
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Earlier work this paper cites.
Kernel Methods for Deep Learning
Cho, Y. and Saul, L. K. (2009) · 2009
Earlier work this paper cites.
Practical Variational Inference for Neural Networks
Graves, A. (2011) · 2011
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MCMC-based image reconstruction with uncertainty quantification
Bardsley, J. M. (2012) · 2012
Earlier work this paper cites.
Ensemble Randomized Maximum Likelihood Method as an Iterative Ensemble Smoother
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Randomize-Then-Optimize: A Method for Sampling from Posterior Distributions in Nonlinear Inverse Problems
Bardsley, J. M., Solonen, A., Haario, H., and Laine, M. (2014) · 2014
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Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Gal, Y. and Ghahramani, Z. (2015) · 2015
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Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
Hernández-Lobato, J. M. and Adams, R. P. (2015) · 2015
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Early Stopping as Nonparametric Variational Inference
Duvenaud, D., Maclaurin, D., and Adams, R. P. (2016) · 2016
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Uncertainty in Deep Learning
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On the Importance of Strong Baselines in Bayesian Deep Learning
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Randomized Prior Functions for Deep Reinforcement Learning
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A Scalable Laplace Approximation for Neural Networks
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Understanding Measures of Uncertainty for Adversarial Example Detection
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The limits and potentials of deep learning for robotics
Sünderhauf, N., Brock, O., Scheirer, W., Hadsell, R., Fox, D., Leitner, J., Upcroft, B., Abbeel, P., Burgard, W., Milford, M., and Corke, P. (2018) · 2018
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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
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Polynomial Regression As an Alternative to Neural Nets
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Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
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Invariance of Weight Distributions in Rectified MLPs
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’In-Between’ Uncertainty in Bayesian Neural Networks
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