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We explore the family of methods "PAC-Bayes with Backprop" (PBB) to train probabilistic neural networks by minimizing PAC-Bayes bounds.
A useful theorem for nonlinear devices having Gaussian inputs
Robert Price · 1958
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Keeping neural networks simple
Geoffrey E Hinton and Drew van Camp · 1993
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Bayesian Learning via Stochastic Dynamics
Radford M Neal · 1993
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Self bounding learning algorithms
Yoav Freund · 1998
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Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping
Rich Caruana, Steve Lawrence, and C Lee Giles · 2001
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(Not) bounding the true error
John Langford and Rich Caruana · 2001
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Bounds for averaging classifiers
John Langford and Matthias Seeger · 2001
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PAC-Bayesian Generalization Error Bounds for Gaussian Process Classification
Matthias Seeger · 2002
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Microchoice bounds and self bounding learning algorithms
John Langford and Avrim Blum · 2003
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A note on the PAC Bayesian theorem
Andreas Maurer · 2004
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Distribution-dependent PAC-Bayes priors
Guy Lever, François Laviolette, and John Shawe-Taylor · 2010
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Stochastic gradient descent tricks
Léon Bottou · 2012
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Concentration inequalities: A nonasymptotic theory of independence
Stéphane Boucheron, Gábor Lugosi, and Pascal Massart · 2013
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Tighter PAC-Bayes bounds through distribution-dependent priors
Guy Lever, François Laviolette, and John Shawe-Taylor · 2013
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Regularization of neural networks using dropconnect
Li Wan, Matthew Zeiler, Sixin Zhang, Yann Le Cun, and Rob Fergus · 2013
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Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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UCI Machine Learning Repository, 2017
Dheeru Dua and Casey Graff · 2017
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Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data
Gintare Karolina Dziugaite and Daniel M. Roy · 2017
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A PAC-Bayesian analysis of randomized learning with application to stochastic gradient descent
Ben London · 2017
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PAC-Bayesian Margin Bounds for Convolutional Neural Networks
Konstantinos Pitas, Mike Davies, and Pierre Vandergheynst · 2017
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A strongly quasiconvex PAC-Bayesian bound
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Robust forward algorithms via PAC-Bayes and Laplace distributions
Asaf Noy and Koby Crammer · 2014
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Understanding Machine Learning
Shai Shalev-Shwartz and Shai Ben-David · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Cited alongside, same era.
Exploring generalization in deep learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro
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A PAC-Bayesian approach to spectrally-normalized margin bounds for neural networks
Behnam Neyshabur, Srinadh Bhojanapalli, and Nathan Srebro
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Niklas Thiemann, Christian Igel, Olivier Wintenberger, and Yevgeny Seldin · 2017
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Data-dependent PAC-Bayes priors via differential privacy
Gintare Karolina Dziugaite and Daniel M Roy · 2018
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Pathwise Derivatives Beyond the Reparameterization Trick
Martin Jankowiak and Fritz Obermeyer · 2018
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Chiyuan Zhang, Samy Bengio, and Yoram Singer · 2019
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