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..
Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness
Authors: Chihuang Liu, Joseph JaJa
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate our model on the MNIST, CIFAR10 and CIFAR-100 datasets, and present empirical justification to attention module and some quantitative and qualitative results. |
| Researcher Affiliation | Academia | Chihuang Liu and Joseph Ja Ja Institute for Advanced Computer Studies and Department of Electrical and Computer Engineering University of Maryland, College Park, MD 20742, USA EMAIL |
| Pseudocode | No | The paper provides mathematical formulations and descriptions of its model, but no explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that its source code is open-sourced or publicly available. |
| Open Datasets | Yes | Our model is evaluated on the MNIST, CIFAR-10, and CIFAR-100 datasets |
| Dataset Splits | No | The paper mentions training and testing phases and refers to the 'test set' but does not specify detailed train/validation/test splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU or GPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We use a CNN with two convolutional layers with 32 and 64 filters respectively, followed by two fully connected layers of size 1024 and 10. The network is trained with 40-step PGD adversary with a step size of 0.01 and l bound of = 0.3. The settings are the same as in Madry et al. [2017]. |