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Bayesian neural networks (BNNs) are a principled approach to modeling predictive uncertainties in deep learning, which are important in safety-critical applications.
On the algebraic structure of feedforward network weight spaces
Robert Hecht-Nielsen · 1990
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Basic algebra
Anthony W Knapp · 2007
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Mcmc using hamiltonian dynamics
Radford M Neal et al · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Improving the identifiability of neural networks for bayesian inference
Arya A Pourzanjani, Richard M Jiang, and Linda R Petzold · 2017
Cited alongside, same era.
A scalable laplace approximation for neural networks
Hippolyt Ritter, Aleksandar Botev, and David Barber · 2018
Cited alongside, same era.
Johanni Brea, Berfin Simsek, Bernd Illing, and Wulfram Gerstner · 2019
Cited alongside, same era.
Linear mode connectivity and the lottery ticket hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, and Michael Carbin · 2020
Cited alongside, same era.
Being bayesian, even just a bit, fixes overconfidence in relu networks
Agustinus Kristiadi, Matthias Hein, and Philipp Hennig · 2020
Cited alongside, same era.
On symmetries in variational bayesian neural nets
Richard Kurle, Tim Januschowski, Jan Gasthaus, and Yuyang Bernie Wang · 2021
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The role of permutation invariance in linear mode connectivity of neural networks
Rahim Entezari, Hanie Sedghi, Olga Saukh, and Behnam Neyshabur · 2022
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Post-training neural network compression with variational bayesian quantization
Zipei Tan and Robert Bamler · 2022
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Evaluating approximate inference in bayesian deep learning
Andrew Gordon Wilson, Pavel Izmailov, Matthew D Hoffman, Yarin Gal, Yingzhen Li, Melanie F Pradier, Sharad Vikram, Andrew Foong, Sanae Lotfi, and Sebastian Farquhar · 2022
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Git re-basin: Merging models modulo permutation symmetries
Samuel Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa · 2023
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Towards efficient mcmc sampling in bayesian neural networks by exploiting symmetry
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Yibo Yang, Robert Bamler, and Stephan Mandt · 2020
Cited alongside, same era.
What are bayesian neural network posteriors really like?
Pavel Izmailov, Sharad Vikram, Matthew D Hoffman, and Andrew Gordon Gordon Wilson · 2021
Cited alongside, same era.
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, and David Rügamer · 2023
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