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<titleInfo><title>On the verification of neural ODEs with stochastic guarantees</title></titleInfo>

  
  
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  <title>Technical Tracks</title>
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  <namePart type="given">Sophie</namePart>
  <namePart type="family">Grunbacher</namePart>
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  <namePart type="given">Ramin</namePart>
  <namePart type="family">Hasani</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
<name type="personal">
  <namePart type="given">Mathias</namePart>
  <namePart type="family">Lechner</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">3DC22916-F248-11E8-B48F-1D18A9856A87</identifier></name>
<name type="personal">
  <namePart type="given">Jacek</namePart>
  <namePart type="family">Cyranka</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
<name type="personal">
  <namePart type="given">Scott A</namePart>
  <namePart type="family">Smolka</namePart>
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  <namePart type="given">Radu</namePart>
  <namePart type="family">Grosu</namePart>
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  <namePart>AAAI: Association for the Advancement of Artificial Intelligence</namePart>
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  <namePart>Formal methods for the design and analysis of complex systems</namePart>
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<abstract lang="eng">We show that Neural ODEs, an emerging class of timecontinuous neural networks, can be verified by solving a set of global-optimization problems. For this purpose, we introduce Stochastic Lagrangian Reachability (SLR), an
abstraction-based technique for constructing a tight Reachtube (an over-approximation of the set of reachable states
over a given time-horizon), and provide stochastic guarantees in the form of confidence intervals for the Reachtube bounds. SLR inherently avoids the infamous wrapping effect (accumulation of over-approximation errors) by performing local optimization steps to expand safe regions instead of repeatedly forward-propagating them as is done by deterministic reachability methods. To enable fast local optimizations, we introduce a novel forward-mode adjoint sensitivity method to compute gradients without the need for backpropagation. Finally, we establish asymptotic and non-asymptotic convergence rates for SLR.</abstract>

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<originInfo><publisher>AAAI Press</publisher><dateIssued encoding="w3cdtf">2021</dateIssued><place><placeTerm type="text">Virtual</placeTerm></place>
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<language><languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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<relatedItem type="host"><titleInfo><title>Proceedings of the AAAI Conference on Artificial Intelligence</title></titleInfo>
  <identifier type="issn">2159-5399</identifier>
  <identifier type="eIssn">2374-3468</identifier>
  <identifier type="isbn">978-1-57735-866-4</identifier>
  <identifier type="arXiv">2012.08863</identifier>
<part><detail type="volume"><number>35</number></detail><detail type="issue"><number>13</number></detail><extent unit="pages">11525-11535</extent>
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<bibliographicCitation>
<mla>Grunbacher, Sophie, et al. “On the Verification of Neural ODEs with Stochastic Guarantees.” &lt;i&gt;Proceedings of the AAAI Conference on Artificial Intelligence&lt;/i&gt;, vol. 35, no. 13, AAAI Press, 2021, pp. 11525–35.</mla>
<apa>Grunbacher, S., Hasani, R., Lechner, M., Cyranka, J., Smolka, S. A., &amp;#38; Grosu, R. (2021). On the verification of neural ODEs with stochastic guarantees. In &lt;i&gt;Proceedings of the AAAI Conference on Artificial Intelligence&lt;/i&gt; (Vol. 35, pp. 11525–11535). Virtual: AAAI Press.</apa>
<short>S. Grunbacher, R. Hasani, M. Lechner, J. Cyranka, S.A. Smolka, R. Grosu, in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 11525–11535.</short>
<ieee>S. Grunbacher, R. Hasani, M. Lechner, J. Cyranka, S. A. Smolka, and R. Grosu, “On the verification of neural ODEs with stochastic guarantees,” in &lt;i&gt;Proceedings of the AAAI Conference on Artificial Intelligence&lt;/i&gt;, Virtual, 2021, vol. 35, no. 13, pp. 11525–11535.</ieee>
<chicago>Grunbacher, Sophie, Ramin Hasani, Mathias Lechner, Jacek Cyranka, Scott A Smolka, and Radu Grosu. “On the Verification of Neural ODEs with Stochastic Guarantees.” In &lt;i&gt;Proceedings of the AAAI Conference on Artificial Intelligence&lt;/i&gt;, 35:11525–35. AAAI Press, 2021.</chicago>
<ama>Grunbacher S, Hasani R, Lechner M, Cyranka J, Smolka SA, Grosu R. On the verification of neural ODEs with stochastic guarantees. In: &lt;i&gt;Proceedings of the AAAI Conference on Artificial Intelligence&lt;/i&gt;. Vol 35. AAAI Press; 2021:11525-11535.</ama>
<ista>Grunbacher S, Hasani R, Lechner M, Cyranka J, Smolka SA, Grosu R. 2021. On the verification of neural ODEs with stochastic guarantees. Proceedings of the AAAI Conference on Artificial Intelligence. AAAI: Association for the Advancement of Artificial Intelligence, Technical Tracks, vol. 35, 11525–11535.</ista>
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