However, many use cases of neural network verification require solving the inverse problem, or over-approximating the set of inputs that lead to certain outputs.

To address these shortcomings, we introduce a pioneering technique that replaces the spatial model with a topological one within the RANSAC process.

To facilitate this procedure, we propose Natural Program, a natural language-based deductive reasoning format. Our approach enables models to generate precise reasoning steps where subsequent steps are more rigorously grounded on prior steps.

We are deeply appreciative of the reviewers for their feedback amidst these trying circumstances. To summarize, DeWeight is indeed the state of the art technique for benchmarks with large tilt. To the best of our knowledge, we are not aware of any practical applications of discrete integration that have small tilt. As mentioned on line 219, we tested our tool on 1056 formulas arising from the domain of neural network verification. These formulas evaluate robustness, trojan attack effectiveness, and fairness of a binarized neural network.

One reason for this is the intractability of analyzing NNs, ODEs and hybrid systems.