An introduction to Bayesian neural networks and uncertainty quantification in neural networks

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However, for many safety-critical applications, as long as scientific research fields, the quantification of the uncertainty of DL model predictions plays a crucial role. In this lecture, I will introduce the basics of Bayesian Neural Networks, how they can tackle the problem of estimating model uncertainty, and the most common techniques for generalizing this method to deep neural networks. Talpini, Jacopo" /> <meta name="keywords" content="CERN Document Server, WebSearch, CERN Document Server" /> <script type="text/javascript" src="https://cds.cern.ch/js/jquery.min.js"></script>  <link rel="stylesheet" href="https://cds.cern.ch/img/webnews.css" type="text/css" />  <script type="text/javascript" src="https://cds.cern.ch/js/webnews.js?v=20131009"></script> <meta property="fb:app_id" content="137353533001720"/> <script type="text/x-mathjax-config"> MathJax.Hub.Config({ tex2jax: {inlineMath: [['$','$']], processEscapes: true}, showProcessingMessages: false, messageStyle: "none" }); </script> <script src="/MathJax/MathJax.js?config=TeX-AMS_CHTML" type="text/javascript"> </script>  <meta content="An introduction to Bayesian neural networks and uncertainty quantification in neural networks" name="citation_title" /> <meta content="2023/03/09" name="citation_publication_date" /> <meta name="citation_online_date" content="2023/03/10">  <meta content="An introduction to Bayesian neural networks and uncertainty quantification in neural networks" property="og:title" /> <meta content="Inverted CERN School of Computing 2023" property="og:title" /> <meta content="website" property="og:type" /> <meta content="website" property="og:type" /> <meta content="https://cds.cern.ch/record/2852107" property="og:url" /> <meta property="og:video:height" content="360" /> <meta property="og:video:width" content="640" /> <meta property="og:video" content="https://lecturemedia.cern.ch/2023/1208723c37/1208723c37-presentation-480p-quality.mp4" /> <meta property="og:video:type" content="video/mp4" /> <meta property="og:image" content="https://lecturemedia.cern.ch/2023/1208723c37/1208723c37-presentation-cover.jpg" /> <meta name="twitter:player:height" content="360" /> <meta name="twitter:player:width" content="640" /> <link rel="image_src" href="https://lecturemedia.cern.ch/2023/1208723c37/1208723c37-presentation-cover.jpg" /> <link rel="video_src" href="https://cds.cern.ch/mediaplayer.swf?file=/1208723c37/1208723c37-presentation-480p-quality.mp4&streamer=rtmp://wowza.cern.ch:1935/vod&provider=rtmp&stretching=exactfit&image=https://lecturemedia.cern.ch/2023/1208723c37/1208723c37-presentation-cover.jpg"/> <meta name="twitter:player" content="https://cds.cern.ch/video/?"/> <meta content="CERN Document Server" property="og:site_name" /> <meta content="Over the past few years, many advances in the field of Deep Learning (DL) have been achieved and nowadays modern DL models are starting to be deployed in our everyday life. 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'Less..' : 'More..'); return false; }); }); </script><table id="download_movie_box"><tr><td align="right" valign="top" id="download_movie_box_format_label">Mp4:</td><td><a id="download_movie_box_internal_link" href="https://lecturemedia.cern.ch/2023/1208723c37/1208723c37-presentation-480p-quality.mp4">360p<br/><span>(presentation)</span></a></td><td><a id="download_movie_box_internal_link" href="https://lecturemedia.cern.ch/2023/1208723c37/1208723c37-presentation-1080p-quality.mp4">1080p<br/><span>(presentation)</span></a></td><td><a id="download_movie_box_internal_link" href="https://lecturemedia.cern.ch/2023/1208723c37/1208723c37-presentation-720p-quality.mp4">720p<br/><span>(presentation)</span></a></td><td><a id="download_movie_box_internal_link" href="https://lecturemedia.cern.ch/2023/1208723c37/1208723c37-presentation-360p-quality.mp4">270p<br/><span>(presentation)</span></a></td><td><a id="download_movie_box_internal_link" href="https://lecturemedia.cern.ch/2023/1208723c37/1208723c37-presenter-480p-quality.mp4">360p<br/><span>(presenter)</span></a></td><td><a id="download_movie_box_internal_link" href="https://lecturemedia.cern.ch/2023/1208723c37/1208723c37-presenter-720p-quality.mp4">720p<br/><span>(presenter)</span></a></td><td><a id="download_movie_box_internal_link" href="https://lecturemedia.cern.ch/2023/1208723c37/1208723c37-presenter-1080p-quality.mp4">1080p<br/><span>(presenter)</span></a></td><td><a id="download_movie_box_internal_link" href="https://lecturemedia.cern.ch/2023/1208723c37/1208723c37-presenter-360p-quality.mp4">270p<br/><span>(presenter)</span></a></td></table> </div> <div id="video_detailbox_embed" style="margin:10px"> <style type="text/css"> #embed_video_box{ background-color: #EEE; border: #DDD solid 1px; margin:0 auto; width: 90%; } #embed_video_box_header{ text-align: center; font-size:small; } </style><table id="embed_video_box"> <tr><th colspan="4" id="embed_video_box_header">Copy-paste this code into your page: </th></tr> <tr><td><textarea readonly="readonly" rows="3" cols="60"><iframe width="640" height="360" frameborder="0" src="https://cds.cern.ch/video/2852107?" allowfullscreen></iframe></textarea></td></tr> </table> </div> <tr><td class="formatRecordLabel"> <span style="white-space:nowrap;">Author(s)</span> </td><td style="padding-left:5px;"><a href="https://cds.cern.ch/search?f=author&p=Talpini%2C%20Jacopo&ln=en">Talpini, Jacopo</a> (speaker) (Università degli Studi di Milano-Bicocca)</td></tr> <tr><td class="formatRecordLabel"> Corporate <span style="white-space:nowrap;">author(s)</span> </td><td style="padding-left:5px;">CERN. Geneva</td></tr>  <script type="text/javascript"> /* * Changes the Imprint title to Date - Duration as requested by RQF0820114 * This is a hack not to touch the general imprint format element */ $(document).ready(function() { var collections = 'Indico'; if (collections.indexOf('E-LEARNING') > -1) { $('.cern-imprint-label').text('Date - Duration'); } }) </script> <tr><td class="formatRecordLabel cern-imprint-label"> Imprint </td><td style="padding-left:5px;">2023-03-09. - 2836.</td></tr>  <tr><td class="formatRecordLabel"> Series </td><td style="padding-left:5px;">(<a href="/search?f=490__a&p=Inverted%20CSC">Inverted CSC</a>)<br/>(<a href="/search?f=490__a&p=Inverted%20CERN%20School%20of%20Computing%202023">Inverted CERN School of Computing 2023</a>)</td></tr> <tr><td class="formatRecordLabel"> Lecture note </td><td style="padding-left:5px;"> on 2023-03-09T10:00:00<br/></td></tr> <tr><td class="formatRecordLabel"> Subject category </td><td style="padding-left:5px;">Inverted CSC</td></tr> <tr><td class="formatRecordLabel"> Abstract </td><td style="padding-left:5px;">Over the past few years, many advances in the field of Deep Learning (DL) have been achieved and nowadays modern DL models are starting to be deployed in our everyday life. However, for many safety-critical applications, as long as scientific research fields, the quantification of the uncertainty of DL model predictions plays a crucial role. In this lecture, I will introduce the basics of Bayesian Neural Networks, how they can tackle the problem of estimating model uncertainty, and the most common techniques for generalizing this method to deep neural networks.</td></tr> <tr><td class="formatRecordLabel"> Copyright/License </td><td style="padding-left:5px;"><a href="https://copyright.web.cern.ch/">© 2023-2024 CERN</a></td></tr> <tr><td class="formatRecordLabel"> Submitted by </td><td style="padding-left:5px;"><a href="mailto:sebastian.lopienski@cern.ch">sebastian.lopienski@cern.ch</a></td></tr> </table> <br/><div><div style="clear: both;"> </div></div> <script type="text/javascript"> // Initially hide: $(".longCaption").hide(); // Allow to toggle visibility: $(".toggleLongCaption").toggle(function(){ $(this).siblings(".longCaption").show('fast'); var thisElem = $(this); thisElem.text(thisElem.text() === "more" ? 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An introduction to Bayesian neural networks and uncertainty quantification in neural networks - CERN Document Server