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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
A Unified View of Piecewise Linear Neural Network Verification
Authors: Rudy R. Bunel, Ilker Turkaslan, Philip Torr, Pushmeet Kohli, Pawan K. Mudigonda
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use the benchmark to provide the first experimental comparison of existing algorithms and identify the factors impacting the hardness of verification problems. |
| Researcher Affiliation | Collaboration | Rudy Bunel University of Oxford EMAIL Ilker Turkaslan University of Oxford EMAIL Philip H.S. Torr University of Oxford EMAIL Pushmeet Kohli Deepmind EMAIL M. Pawan Kumar University of Oxford Alan Turing Institute EMAIL |
| Pseudocode | Yes | Algorithm 1 Branch and Bound |
| Open Source Code | Yes | All code and data necessary to replicate our analysis are released. |
| Open Datasets | Yes | The Collision Detection data set [6] attempts to predict whether two vehicles with parameterized trajectories are going to collide. |
| Dataset Splits | No | The paper refers to using datasets like Collision Detection, ACAS, and PCAMNIST, but does not provide specific details on how these datasets were split into training, validation, or test sets (e.g., percentages, sample counts, or explicit references to standard splits used). |
| Hardware Specification | Yes | We attempt to verify each property with a timeout of two hours, and a maximum allowed memory usage of 20GB, on a single core of a machine with an i7-5930K CPU. |
| Software Dependencies | No | The paper mentions software like 'Python', 'Gurobi', and 'GLPK library' but does not provide specific version numbers for these software dependencies, which are necessary for full reproducibility. |
| Experiment Setup | Yes | The base network has 10 inputs and 4 layers of 25 hidden units, and the property to prove is True with a margin of 1000. Each of the plot correspond to a variation of one of this parameters. |