A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses
Authors: Ambar Pal, Rene Vidal
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments are conducted on the MNIST and FMNIST datasets restricted to two classes. We train a 4-layer convolutional neural network with ReLU activation functions for this binary classification task. The classification results are shown in Table 1, from which we can draw two main conclusions: |
| Researcher Affiliation | Academia | Ambar Pal Mathematical Institute for Data Science Johns Hopkins University ambar@jhu.edu René Vidal Mathematical Institute for Data Science Johns Hopkins University rvidal@jhu.edu |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper's main text. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Our experiments are conducted on the MNIST and FMNIST datasets restricted to two classes. |
| Dataset Splits | No | The paper mentions using a 'finite training set' and discusses 'generalization bounds', but does not explicitly specify the training, validation, or test dataset splits (e.g., percentages or sample counts) needed for reproduction. |
| Hardware Specification | No | No specific hardware (e.g., GPU/CPU models, memory details) used for running the experiments was mentioned in the paper. |
| Software Dependencies | No | The paper mentions training a '4-layer convolutional neural network with ReLU activation functions' but does not specify any software dependencies (e.g., PyTorch, TensorFlow) with version numbers. |
| Experiment Setup | No | The paper states 'A detailed description, as well as more experimental details can be found in Sec. D of the Appendix.', indicating that some details exist elsewhere in the paper, but the provided text itself does not contain specific experimental setup details such as hyperparameters or training configurations. |