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The Geometry of Neural Nets' Parameter Spaces Under Reparametrization


Feb 14, 2023
Agustinus Kristiadi, Felix Dangel, Philipp Hennig

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Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks


May 20, 2022
Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig

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Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference


Mar 07, 2022
Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg

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* 24 pages, 9 figues, to be published in AISTATS22 

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Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning


Nov 05, 2021
Runa Eschenhagen, Erik Daxberger, Philipp Hennig, Agustinus Kristiadi

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* Bayesian Deep Learning Workshop, NeurIPS 2021 

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Laplace Redux -- Effortless Bayesian Deep Learning


Jun 28, 2021
Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig

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* Source Code: https://github.com/AlexImmer/Laplace; Library Documentation: https://aleximmer.github.io/Laplace/ 

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Being a Bit Frequentist Improves Bayesian Neural Networks


Jun 18, 2021
Agustinus Kristiadi, Matthias Hein, Philipp Hennig

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Learnable Uncertainty under Laplace Approximations


Oct 06, 2020
Agustinus Kristiadi, Matthias Hein, Philipp Hennig

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Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features


Oct 06, 2020
Agustinus Kristiadi, Matthias Hein, Philipp Hennig

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Fast Predictive Uncertainty for Classification with Bayesian Deep Networks


Mar 02, 2020
Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig

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Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks


Feb 24, 2020
Agustinus Kristiadi, Matthias Hein, Philipp Hennig

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