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GoTube: Scalable Stochastic Verification of Continuous-Depth Models [article]

Sophie Gruenbacher, Mathias Lechner, Ramin Hasani, Daniela Rus, Thomas A. Henzinger, Scott Smolka, Radu Grosu
2021 arXiv   pre-print
We introduce a new stochastic verification algorithm that formally quantifies the behavioral robustness of any time-continuous process formulated as a continuous-depth model.  ...  GoTube is implemented in JAX and optimized to scale to complex continuous-depth neural network models.  ...  Continuous-depth models.  ... 
arXiv:2107.08467v2 fatcat:lird25izyjc6rb6qk3zaqw753y

GoTube: Scalable Statistical Verification of Continuous-Depth Models

Sophie A. Gruenbacher, Mathias Lechner, Ramin M. Hasani, Daniela Rus, Thomas A. Henzinger, Scott A. Smolka, Radu Grosu
2022 AAAI Conference on Artificial Intelligence  
GoTube is implemented in JAX and optimized to scale to complex continuous-depth neural network models.  ...  We introduce a new statistical verification algorithm that formally quantifies the behavioral robustness of any timecontinuous process formulated as a continuous-depth model.  ...  Continuous-depth models.  ... 
dblp:conf/aaai/GruenbacherLHRH22 fatcat:xurpydmw2feoviifppsez2d4yu

Reachability Analysis of a General Class of Neural Ordinary Differential Equations [article]

Diego Manzanas Lopez, Patrick Musau, Nathaniel Hamilton, Taylor T. Johnson
2022 arXiv   pre-print
Continuous deep learning models, referred to as Neural Ordinary Differential Equations (Neural ODEs), have received considerable attention over the last several years.  ...  Specifically, our work extends an existing neural network verification tool to support neural ODEs.  ...  Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of AFOSR, DARPA, or NSF.  ... 
arXiv:2207.06531v1 fatcat:bsjgudckazaftcfne56hvgbjem