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Hello,</div>
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A friendly reminder: <a title="https://naesseth.github.io/" id="LPlnk118684" href="https://naesseth.github.io/">
Christian Naesseth</a> will give a seminar this afternoon at 13:30 in Alan Turing</div>
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Best regards,</div>
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Louis</div>
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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" style="font-size:11pt" color="#000000"><b>Från:</b> Louis Ohl <louis.ohl@liu.se><br>
<b>Skickat:</b> den 12 augusti 2025 14:25<br>
<b>Till:</b> ml-seminars@lists.liu.se <ml-seminars@lists.liu.se>; ai-academy@groups.liu.se <ai-academy@groups.liu.se>; idaint@ida.liu.se <idaint@ida.liu.se><br>
<b>Ämne:</b> Machine Learning Seminar, 10/9 at 13:30: Christian Naesseth, "SDE Matching: Scalable Variational Inference for Stochastic Differential Equations" </font>
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<h1 class="x_elementToProof" style="direction:ltr; text-align:left; text-indent:0px; margin-top:12pt; margin-right:0px; margin-left:0px; font-family:"Aptos Display"; font-size:16pt; font-weight:normal; color:rgb(24,90,189)">
Welcome to an IDA Machine Learning Seminar on Wednesday, September 10 at 13:30 in Alan Turing</h1>
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<b>SDE Matching: Scalable Variational Inference for Stochastic Differential Equations</b></div>
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<b><a title="https://naesseth.github.io/" class="x_OWAAutoLink" id="LPlnk921655" href="https://naesseth.github.io/">Christian Naesseth</a>, University of Amsterdam</b></div>
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<a target="_blank" id="LPUrlAnchor579919" href="https://naesseth.github.io/" style="text-decoration:none">Elemental AI | Christian A. Naesseth</a></div>
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CAN Lab, Informatics Institute, University of AmsterdamI am an Assistant Professor of Machine Learning at the University of Amsterdam, a member of the Amsterdam Machine Learning Lab, the lab manager of the UvA-Bosch Delta Lab 2, and an ELLIS member. My research
interests span generative modeling, uncertainty quantification, reasoning, and machine learning, as well as their application to the ...</div>
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naesseth.github.io</div>
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<i>Abstract:</i> The Latent Stochastic Differential Equation (SDE) is a powerful tool for time series and sequence modeling. However, training Latent SDEs typically relies on adjoint sensitivity methods, which depend on simulation, discretisation, and backpropagation
through approximate SDE solutions, which limit scalability. In this work, we propose SDE Matching, a new simulation- and discretisation-free method for training Latent SDEs. Inspired by modern Score- and Flow Matching algorithms for learning generative dynamics,
we extend these ideas to the domain of stochastic dynamics for time series and sequence modeling, eliminating the need for costly numerical simulations. Our results demonstrate that SDE Matching achieves performance comparable to adjoint sensitivity methods
while drastically reducing computational complexity.</div>
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<span style="color:rgb(0,0,0)">Location: <b>Alan Turing,</b> </span><span style="color:rgb(70,120,134)"><u><a rel="noreferrer noopener" class="x_Hyperlink x_SCXW237411067 x_BCX2 x_OWAAutoLink" id="OWAbaf874be-ee15-9f32-44dd-012ce3808530" target="_blank" href="https://www.ida.liu.se/department/location/search.en.shtml?keyword=alan" style="color:rgb(70,120,134)">https://www.ida.liu.se/department/location/search.en.shtml?keyword=alan</a></u></span><span style="color:rgb(0,0,0)"> </span></div>
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<span style="color:rgb(0,0,0)">The list of future seminars in the series is available at: <br>
</span><span style="color:rgb(70,120,134)"><u><a rel="noreferrer noopener" class="x_Hyperlink x_SCXW237411067 x_BCX2 x_OWAAutoLink" id="OWAd8f80309-ecdb-5cdc-4523-6c4c57388171" target="_blank" href="https://stima.gitlab-pages.liu.se/ml-seminars/" style="color:rgb(70,120,134)">https://stima.gitlab-pages.liu.se/ml-seminars/</a></u></span><span style="color:rgb(0,0,0)"> </span></div>
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<span style="color:rgb(0,0,0)">You can subscribe to the seminar series' calendar using this ics link:
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<b>Louis Ohl</b></div>
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louis.ohl@liu.se</div>
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<b>Linköping University</b></div>
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IDA / STIMA</div>
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B-Huset, 2A:445</div>
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