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Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty quantification and model selection.
The approximation of one matrix by another of lower rank
Carl Eckart and Gale Young · 1936
Earlier work this paper cites.
Sequential Updating of Conditional Probabilities on Directed Graphical Structures
David J Spiegelhalter and Steffen L Lauritzen · 1990
Earlier work this paper cites.
Optimal Brain Damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
Earlier work this paper cites.
Transforming Neural-Net Output Levels to Probability Distributions
John S Denker and Yann LeCun · 1990
Earlier work this paper cites.
Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights
Geoffrey E Hinton and Drew Van Camp · 1993
Earlier work this paper cites.
Probable Networks and Plausible Predictions—a Review of Practical Bayesian Methods for Supervised Neural Networks
David JC MacKay · 1995
Earlier work this paper cites.
Bayesian Gaussian Processes for Regression and Classification
Mark N Gibbs · 1997
Earlier work this paper cites.
Bayesian Classification with Gaussian processes
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Nicol N Schraudolph · 2002
Earlier work this paper cites.
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Christopher M. Bishop · 2006
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José M Bernardo and Adrian FM Smith · 2009
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An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
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Weight Uncertainty in Neural Networks
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Deterministic Variational Inference for Robust Bayesian Neural Networks
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Overcoming Catastrophic Forgetting in Neural Networks
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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
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Attention is All You Need
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Official Code: Estimating Model Uncertainty of Neural Networks in Sparse Information Form, ICML2020
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Functional Regularisation for Continual Learning with Gaussian Processes
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Continual Deep Learning by Functional Regularisation of Memorable Past
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Bayesian Deep Learning and a Probabilistic Perspective of Generalization
Andrew G Wilson and Pavel Izmailov · 2020
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Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations
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Bayesian-Torch: Bayesian Neural Network Layers for Uncertainty Estimation
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Bayesian Deep Learning via Subnetwork Inference
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