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	<title>Hub Tobias Wöhrer</title>
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	<link>https://dcn.nat.fau.eu</link>
	<description>FAU DCN-AvH. Chair for Dynamics, Control, Machine Learning and Numerics -Alexander von Humboldt Professorship</description>
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	<title>Hub Tobias Wöhrer</title>
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		<title>Training of neural ODEs using pyTorch</title>
		<link>https://dcn.nat.fau.eu/training-of-neural-odes-using-pytorch/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Tue, 13 Sep 2022 14:11:31 +0000</pubDate>
				<category><![CDATA[Hub]]></category>
		<category><![CDATA[Hub Tobias Wöhrer]]></category>
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					<description><![CDATA[Start with tutorials to get familiar with the code Tutorial 1: Train a neural ODE based network on point cloud data set and generating a gif of the resulting time evolution of the neural ODE Code: Code is based on GitHub: borjanG : 2021-dynamical-systems that uses the torchdiffeq package GitHub : rtqichen: torchdiffeq &#160; Code: [&#8230;]]]></description>
		
		
		
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		<title>Robust neural ODEs</title>
		<link>https://dcn.nat.fau.eu/robust-neural-odes/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 20 Jun 2022 14:16:40 +0000</pubDate>
				<category><![CDATA[Hub]]></category>
		<category><![CDATA[Hub Tobias Wöhrer]]></category>
		<guid isPermaLink="false">https://dcn.nat.fau.eu/?p=24813</guid>

					<description><![CDATA[The code implements the gradient regularization method of robust training in the setting of neural ODEs. Various jupyter notebooks are included that generate plots comparing standard to robust training for 2d point clouds. Code: A good starting point is robustness_plots.ipynb Code is based on GitHub: borjanG : 2021-dynamical-systems that uses the torchdiffeq package GitHub : [&#8230;]]]></description>
		
		
		
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