GoTube: Scalable Statistical Verification of Continuous-Depth Models
Authors: Sophie A. Gruenbacher, Mathias Lechner, Ramin Hasani, Daniela Rus, Thomas A. Henzinger, Scott A. Smolka, Radu Grosu6755-6764
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that Go Tube substantially outperforms state-of-the-art verification tools in terms of the size of the initial ball, speed, time-horizon, task completion, and scalability on a large set of experiments. |
| Researcher Affiliation | Academia | 1 TU Wien 2 IST Austria 3 CSAIL MIT 4 Stony Brook University |
| Pseudocode | Yes | Algorithm 1: Go Tube |
| Open Source Code | Yes | Code / Appendix: https://github.com/Daten Vorsprung/Go Tube |
| Open Datasets | No | The paper names standard benchmarks like 'Cart Pole-v1' and classical dynamical systems. While these are commonly understood to be public, the paper does not provide concrete access information (specific links, DOIs, or formal citations with authors/year) for these datasets/environments themselves within the text. |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits. The experiments are conducted on continuous dynamical systems and neural models, where the concept of data splits as in typical supervised learning tasks does not directly apply. |
| Hardware Specification | Yes | We run our evaluations on a standard workstation machine setup (12 v CPUs, 64GB memory) equipped with a single GPU for a per-run timeout of 1 hour (except for runtimes reported in Figure 4). |
| Software Dependencies | No | The paper mentions 'JAX' as an implementation tool but does not provide specific version numbers for JAX or any other software dependencies. It also refers to 'advanced automatic differential toolboxes'. |
| Experiment Setup | No | The paper mentions some general settings like 'ยต = 1.1 as the tightness factor' and '99% confidence level' but does not provide a comprehensive experimental setup, including specific hyperparameters (e.g., learning rates, batch sizes), model initialization details, or other system-level training settings. |