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<p class="MsoNormal"><span lang="EN-US" style="font-size:14.0pt">Next week we will have a bonus Machine Learning seminar by Paweł Morkisz, Wednesday November 15 at 15:15 in
<b>Alan Turing</b><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">Deep learning-based estimation of time-dependent parameters in Markov models with application to SDEs - machine learning aspects and experiments details</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 lang="EN-US" style="font-size:16.0pt;color:black"><a href="https://scholar.google.com/citations?user=E8gToekAAAAJ">Paweł Morkisz</a>, NVIDIA & AGH University of Kraków<o:p></o:p></span></b></p>
<p class="MsoNormal"><b><span lang="EN-US" style="font-size:16.0pt;color:black"><o:p> </o:p></span></b></p>
<p class="MsoPlainText"><i><span lang="EN-US">Abstract:</span></i><span lang="EN-US"> We present a novel method for estimation of time-dependent unknown parameters based on discrete sampling of Markov processes using deep learning techniques. Neural networks
have enabled a variety of applications. Usually, we mean machine learning, in which a computer learns to perform some task by analyzing training examples and directly having access to the predicted values.
<o:p></o:p></span></p>
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<p class="MsoPlainText"><span lang="EN-US">In this work, we employ the deep learning framework to solve the problem of approximation of time-dependent parameters from the actual data. The idea is to change this approximation task to an optimization problem
using the maximum likelihood approach and then obtain the appropriate loss function, which can be used to train neural networks. We demonstrate the effectiveness of our approach through a series of numerical experiments using the Deep Learning framework --
TensorFlow. We focus on estimating parameters in multivariate regression and stochastic differential equations (SDEs). Moreover, we base this approach on theoretical results in the SDEs case - we prove that under certain conditions, the solution process of
the underlying SDE with the actual parameter function is close to the SDE with the parameter function obtained from our neural network.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Location: <b>Alan Turing,</b> <a href="https://www.ida.liu.se/department/location/search.en.shtml?keyword=alan">
https://www.ida.liu.se/department/location/search.en.shtml?keyword=alan</a><o:p></o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US">Welcome!!<o:p></o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US">------------------<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"> <o:p></o:p></span></p>
<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>
<p class="MsoNormal"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
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