Merge pull request #77 from JuliaTrustworthyAI/76-revise-extended-abs…

…tract

76 revise extended abstract

paper/Project.toml

@@ -8,6 +8,6 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
+TaijaPlotting = "bd7198b4-c7d6-400c-9bab-9a24614b0240"
 Term = "22787eb5-b846-44ae-b979-8e399b8463ab"

paper/juliacon.cls

@@ -939,6 +939,11 @@ to\hsize{\if@nfeven\else\hfil\fi\box\@tempboxa\if@nfeven\hfil\fi}
 
 \usepackage{authblk}
 
+% Fixes issue related to listing captions: https://github.com/JuliaCon/JuliaConSubmission.jl/issues/31
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+\captionsetup[lstlisting]{singlelinecheck=false, margin=0pt}
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+
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 % end of juliacon.cls

paper/paper.aux

@@ -32,31 +32,44 @@
 \newlabel{sec-intro}{{1}{1}{Background}{section.1}{}}
 \@writefile{toc}{\contentsline {section}{\numberline {2}Laplace Approximation for Deep Learning}{1}{section.2}\protected@file@percent }
 \newlabel{sec-body}{{2}{1}{Laplace Approximation for Deep Learning}{section.2}{}}
-\newlabel{eq-bma}{{1}{1}{Laplace Approximation for Deep Learning}{equation.2.1}{}}
-\newlabel{eq-glm}{{2}{1}{Laplace Approximation for Deep Learning}{equation.2.2}{}}
-\newlabel{eq-glm-predictive}{{3}{1}{Laplace Approximation for Deep Learning}{equation.2.3}{}}
+\newlabel{eq-bma}{{1}{1}{Laplace Approximation for Deep Learning}{section*.3}{}}
+\newlabel{eq-glm}{{2}{1}{Laplace Approximation for Deep Learning}{section*.4}{}}
+\newlabel{eq-glm-predictive}{{3}{1}{Laplace Approximation for Deep Learning}{section*.5}{}}
 \citation{daxberger2021laplace}
+\citation{daxberger2021laplace}
+\citation{martens2020optimizing}
+\citation{antoran2023samplingbased}
 \bibstyle{juliacon}
 \bibdata{ref.bib}
-\bibcite{blundell2015weight}{1}
-\bibcite{daxberger2021laplace}{2}
-\bibcite{gal2016dropout}{3}
-\bibcite{goodfellow2014explaining}{4}
-\bibcite{immer2020improving}{5}
-\bibcite{lakshminarayanan2016simple}{6}
-\bibcite{lawrence2001variational}{7}
-\bibcite{martens2015optimizing}{8}
-\bibcite{wilson2020case}{9}
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-\newlabel{fig-class}{{1}{2}{Posterior predictive distribution for binary classifier: plugin estimate (left), untuned LA (center) and optimized LA (right). The colour of the contour indicates the predicted class probabilities: the more yellow a region, the more confident the classifier that samples belong to the orange class}{figure.1}{}}
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 \newlabel{laplaceredux.jl-a-julia-implementation}{{3}{2}{\texorpdfstring {\texttt {LaplaceRedux.jl} --- a Julia implementation}{LaplaceRedux.jl --- a Julia implementation}}{section.3}{}}
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+\newlabel{lst:fit}{{1}{2}{Fitting a pre-trained neural network to data using Laplace Redux}{lstlisting.1}{}}
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+\newlabel{lst:predict}{{2}{2}{Predictions using the fitted Laplace Redux instance}{lstlisting.2}{}}
+\@writefile{lol}{\contentsline {lstlisting}{\numberline {2}{\ignorespaces Predictions using the fitted Laplace Redux instance.}}{2}{lstlisting.2}\protected@file@percent }
+\newlabel{lst:other}{{3}{2}{Prior optimization and visualization of the predictive distribution}{lstlisting.3}{}}
+\@writefile{lol}{\contentsline {lstlisting}{\numberline {3}{\ignorespaces Prior optimization and visualization of the predictive distribution.}}{2}{lstlisting.3}\protected@file@percent }
+\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Posterior predictive distribution for binary classifier: plugin estimate (left), untuned LA (center) and optimized LA (right). The colour of the contour indicates the predicted class probabilities: the more yellow a region, the more confident the classifier that samples belong to the orange class.\relax }}{2}{figure.caption.6}\protected@file@percent }
+\providecommand*\caption@xref[2]{\@setref\relax\@undefined{#1}}
+\newlabel{fig-class}{{1}{2}{Posterior predictive distribution for binary classifier: plugin estimate (left), untuned LA (center) and optimized LA (right). The colour of the contour indicates the predicted class probabilities: the more yellow a region, the more confident the classifier that samples belong to the orange class.\relax }{figure.caption.6}{}}
+\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Posterior predictive distribution for regressor: wide regions of the confidence interval (shaded area) indicate high predictive uncertainty.\relax }}{2}{figure.caption.7}\protected@file@percent }
+\newlabel{fig-reg}{{2}{2}{Posterior predictive distribution for regressor: wide regions of the confidence interval (shaded area) indicate high predictive uncertainty.\relax }{figure.caption.7}{}}
+\@writefile{toc}{\contentsline {section}{\numberline {4}Scaling Up}{2}{section.4}\protected@file@percent }
+\newlabel{sec-scale}{{4}{2}{Scaling Up}{section.4}{}}
+\@writefile{toc}{\contentsline {section}{\numberline {5}Discussion and Outlook}{2}{section.5}\protected@file@percent }
+\newlabel{sec-con}{{5}{2}{Discussion and Outlook}{section.5}{}}
+\@writefile{toc}{\contentsline {section}{\numberline {6}Acknowledgements}{2}{section.6}\protected@file@percent }
+\newlabel{sec-ack}{{6}{2}{Acknowledgements}{section.6}{}}
+\bibcite{antoran2023samplingbased}{1}
+\bibcite{blundell2015weight}{2}
+\bibcite{daxberger2021laplace}{3}
+\bibcite{gal2016dropout}{4}
+\bibcite{goodfellow2014explaining}{5}
+\bibcite{immer2020improving}{6}
+\bibcite{lakshminarayanan2016simple}{7}
+\bibcite{lawrence2001variational}{8}
+\bibcite{martens2015optimizing}{9}
+\bibcite{martens2020optimizing}{10}
+\bibcite{wilson2020case}{11}
+\@writefile{toc}{\contentsline {section}{\numberline {7}References}{3}{section.7}\protected@file@percent }
+\gdef \@abspage@last{3}

paper/paper.bbl

@@ -1,4 +1,9 @@
-\begin{thebibliography}{1}
+\begin{thebibliography}{10}
+
+\bibitem{antoran2023samplingbased}
+Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric Nalisnick, David Janz, and José~Miguel Hernández-Lobato.
+Sampling-based inference for large linear models, with application to linearised laplace, 2023.
+2210.04994.
 
 \bibitem{blundell2015weight}
 Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra.
@@ -39,6 +44,11 @@ James Martens and Roger Grosse.
 Optimizing neural networks with kronecker-factored approximate curvature.
 In {\em International conference on machine learning}, pages 2408--2417. PMLR, 2015.
 
+\bibitem{martens2020optimizing}
+James Martens and Roger Grosse.
+Optimizing neural networks with kronecker-factored approximate curvature, 2020.
+1503.05671.
+
 \bibitem{wilson2020case}
 Andrew~Gordon Wilson.
 The case for {{Bayesian}} deep learning.

paper/paper.blg

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 Warning--empty note in immer2020improving
 Warning--empty note in lakshminarayanan2016simple
 Warning--empty note in wilson2020case
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