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<p class="MsoNormal"><span lang="EN-US" style="font-size:14.0pt">Welcome to an IDA Machine Learning Seminar on Wednesday, November 8 at 15:15 in Ada Lovelace<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="font-size:16.0pt;color:black">Moment matching denoising Gibbs sampling</span></b><b><span lang="EN-US" style="font-size:16.0pt;color:black"><o:p></o:p></span></b></p>
<p class="MsoNormal"><b><span style="font-size:16.0pt;color:black"><a href="https://tbrx.github.io/"><span lang="EN-US" style="color:#225785;text-decoration:none">Brooks Paige</span></a></span></b><b><span lang="EN-US" style="font-size:16.0pt;color:black">,
Associate Professor, University College London<o:p></o:p></span></b></p>
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<p class="MsoNormal"><i><span lang="EN-US">Abstract:</span></i><span lang="EN-US"> Energy-based models offer a versatile framework for modeling complex data distributions. However, training and sampling from energy-based models poses significant challenges.
The widely-used denoising score matching method for training energy-based models suffers from inconsistency issues, causing the energy model to learn a `noisy' data distribution. In this talk I will describe a proposal for an alternative sampling framework,
(pseudo)-Gibbs sampling with moment matching, which enables effective sampling from the underlying clean model when given a `noisy' model trained via denoising score matching. We explore the benefits of the approach compared to related methods, how to scale
to high-dimensional data, and connections to diffusion models.<o:p></o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US">Location: <b>Ada Lovelace,</b> <a href="https://www.ida.liu.se/department/location/search.en.shtml?keyword=ada">
https://www.ida.liu.se/department/location/search.en.shtml?keyword=ada</a><o:p></o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US">------------------<o:p></o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US">The list of future seminars in the series is available at:
</span><a href="http://www.ida.liu.se/research/machinelearning/seminars/" target="_BLANK2"><span lang="EN-US">http://www.ida.liu.se/research/machinelearning/seminars/</span></a><span lang="EN-US"><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">You can subscribe to the seminar series' calendar using this ics link:
<a href="https://outlook.office365.com/owa/calendar/4d811ae47ce446f58d11a7c2f50a7ed8@ad.liu.se/0f5253d7bc7841248c71eb4c28eb2d668927992292494627279/calendar.ics" target="_BLANK2">
https://outlook.office365.com/owa/calendar/4d811ae47ce446f58d11a7c2f50a7ed8@ad.liu.se/0f5253d7bc7841248c71eb4c28eb2d668927992292494627279/calendar.ics</a><o:p></o:p></span></p>
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