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..
Adversarial robustness via robust low rank representations
Authors: Pranjal Awasthi, Himanshu Jain, Ankit Singh Rawat, Aravindan Vijayaraghavan
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate the improvements obtained by our approach on image data in Section 2. Empirical Evaluation. We compare Algorithm 1 with the algorithm of [12] for various values of σ and ε (used for training to optimize (7)). We train a Res Net-32 network on the CIFAR-10 dataset by optimizing (7). In Figure 2 we present the result of our training procedure for various values of ε and σ and compare with the ℓ2 smoothing method of [12] on the CIFAR-10 and CIFAR-100 datasets. We evaluate our approach on the CIFAR-10 and CIFAR-100 datasets. |
| Researcher Affiliation | Collaboration | Pranjal Awasthi Google Research and Rutgers University. Himanshu Jain Google Research. Ankit Singh Rawat Google Research. Aravindan Vijayaraghavan Northwestern University. |
| Pseudocode | Yes | Algorithm 1 Adversarial training via projections. Algorithm 2 Fast Certification of ℓ1 norm and Quadratic Programming. |
| Open Source Code | No | No explicit statement or link providing access to the open-source code for the methodology described in this paper. |
| Open Datasets | Yes | We train a Res Net-32 network on the CIFAR-10 dataset by optimizing (7). In Figure 2 we present the result of our training procedure for various values of ε and σ and compare with the ℓ2 smoothing method of [12] on the CIFAR-10 and CIFAR-100 datasets. |
| Dataset Splits | No | No explicit details on train/validation/test dataset splits (e.g., percentages, sample counts, or specific split files) are provided in the paper. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are provided in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., 'PyTorch 1.9', 'Python 3.8') are provided in the paper. |
| Experiment Setup | Yes | We compare Algorithm 1 with the algorithm of [12] for various values of σ and ε (used for training to optimize (7)). See Appendix B for a description of the hyperparameters and additional experiments. Following [6, 12], the inner maximization of finding adversarial perturbations is solved via projected gradient descent (PGD), and given the adversarial perturbations, the outer minimization uses stochastic gradient descent. |