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..
Robustness of classifiers: from adversarial to random noise
Authors: Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments and show that the derived bounds provide very accurate estimates when applied to various state-of-the-art deep neural networks and datasets. |
| Researcher Affiliation | Academia | École Polytechnique Fédérale de Lausanne Lausanne, Switzerland {alhussein.fawzi, seyed.moosavi, pascal.frossard} at epfl.ch |
| Pseudocode | No | The paper does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | Le Net (MNIST), Le Net (CIFAR-10), VGG-F (Image Net), VGG-19 (Image Net) |
| Dataset Splits | No | The paper mentions 'test set' (D) for evaluating β(f; m), but it does not provide specific details on training/validation splits, percentages, or sample counts. |
| Hardware Specification | Yes | We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper evaluates pre-existing state-of-the-art classifiers (LeNet, VGG-F, VGG-19) on various datasets but does not provide specific details on hyperparameters, training configurations, or model initialization used for these classifiers in their experiments. |