VNN: Verification-Friendly Neural Networks with Hard Robustness Guarantees
Authors: Anahita Baninajjar, Ahmed Rezine, Amir Aminifar
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we assess the performance of our proposed framework to generate VNNs compared to state-of-the-art techniques. Our framework is implemented using the Gurobi solver (Gurobi Optimization, LLC, 2023) on a Mac Book Pro with an 8-core CPU and 32 GB of RAM1. [...] We consider three public datasets for evaluation of VNNs, namely, the MNIST dataset (Le Cun, 1998) and two datasets from safety-critical medical applications. |
| Researcher Affiliation | Academia | 1Department of Electrical and Information Technology, Lund University, Lund, Sweden, 2Department of Computer and Information Science, Link oping University, Link oping, Sweden. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available on https://github.com/ anahitabn94/VNN. |
| Open Datasets | Yes | We consider three public datasets for evaluation of VNNs, namely, the MNIST dataset (Le Cun, 1998) and two datasets from safety-critical medical applications. The first one is about epileptic seizure detection based on the CHB-MIT Scalp EEG database (Shoeb, 2010) and the second one concerns cardiac arrhythmia detection based on the MITBIH Arrhythmia database (Goldberger et al., 2000). |
| Dataset Splits | Yes | For each patient, we consider 60%, 20%, and 20% of the dataset for the training, validation, and test sets, respectively. [...] The training set includes 75% of the entire dataset. The remaining 25% are equally partitioned among the test set and the validation set. |
| Hardware Specification | Yes | Our framework is implemented using the Gurobi solver (Gurobi Optimization, LLC, 2023) on a Mac Book Pro with an 8-core CPU and 32 GB of RAM1. |
| Software Dependencies | Yes | Our framework is implemented using the Gurobi solver (Gurobi Optimization, LLC, 2023) on a Mac Book Pro with an 8-core CPU and 32 GB of RAM1. |
| Experiment Setup | Yes | Here, we investigate how changing the hyperparameter ϵ impacts verification-friendliness on FNNs characterized by different structures. [...] For example, when ϵ = 0.1, the value of each neuron x(l) i can change in the range of [0.9x(l) i , 1.1x(l) i ]... |