On the Verification of Neural ODEs with Stochastic Guarantees
Authors: Sophie Grunbacher, Ramin Hasani, Mathias Lechner, Jacek Cyranka, Scott A. Smolka, Radu Grosu11525-11535
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we present a thorough theoretical approach to the problem of providing safety guarantees for the class of time-continuous neural networks formulated as Neural ODEs. As the main result, we develop SLR, a differentiable stochastic Lagrangian reachability framework, formulated as a global optimization problem. In particular, we prove that SLR converges (Theorem 2) to tight ellipsoidal safe regions (Theorem 1), within O( ln γ(δ0/rbound)2n) number of iterations (Theorem 3). |
| Researcher Affiliation | Academia | Sophie Gruenbacher1, Ramin Hasani1,2, Mathias Lechner3, Jacek Cyranka4, Scott A. Smolka5, Radu Grosu1 1 Technische Universität Wien (TU Wien) 2 Massachusetts Institute of Technology (MIT) 3 Institute of Science and Technology Austria (IST Austria) 4 University of Warsaw 5 Stony Brook University |
| Pseudocode | Yes | Algorithm 1 Finding the local minimum; Algorithm 2 Computation of ϕL; Algorithm 3 Computing the Radius of the Safety Region; Algorithm 4 Stochastic Lagrangian Reachability |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training on a dataset; therefore, no information about public dataset availability for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and algorithms. It does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and algorithms. It does not describe an empirical experimental setup with hyperparameters or training settings. |