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..
Abstraction based Output Range Analysis for Neural Networks
Authors: Pavithra Prabhakar, Zahra Rahimi Afzal
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results highlight the trade-off between the computation time and the precision of the computed output range. In this section, we present our experimental analysis using a Python toolbox that implements the abstraction procedure and the reduction of the INN output range computation to MILP solving. |
| Researcher Affiliation | Academia | Pavithra Prabhakar , Zahra Rahimi Afzal Department of Computer Science Kansas State University Manhattan, KS 66506 EMAIL |
| Pseudocode | No | The paper describes methods through mathematical equations and textual explanations, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states, 'We have implemented our algorithm in a Python toolbox.' However, it does not provide any concrete access information (e.g., repository link, explicit release statement) for this code. |
| Open Datasets | Yes | We consider as a case study ACAS Xu benchmarks, which are neural networks with 6 hidden layer with each layer consisting of 50 neurons [2]. |
| Dataset Splits | No | The paper mentions using 'ACAS Xu benchmarks' but does not specify any dataset splits (training, validation, or test) for these benchmarks. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'Python toolbox' and 'Gurobi' for MILP solving, but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We consider abstractions of the benchmark with different number of abstract nodes, namely, 2, 4, 8, 16, 32, which are generated randomly. For a fixed number of abstract nodes, we perform 30 different random runs, and measure the average, maximum and minimum time for different parts of the analysis. |