Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |