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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius
Authors: Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, Liwei Wang
ICLR 2020 | Venue PDF | LLM Run Details | Input Tokens: 27,958 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,852 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we show that our method can be applied to modern deep neural networks on a wide range of datasets, including Cifar-10, Image Net, MNIST, and SVHN. |
| Researcher Affiliation | Collaboration | 1Peking University 2CMU 3UCLA 4Google |
| Pseudocode | Yes | Algorithm 1 MACER: robust training via MAximizing CErtified Radius |
| Open Source Code | Yes | Our code is available at https://github.com/Runtian Z/macer. |
| Open Datasets | Yes | In our experiments, we show that our method can be applied to modern deep neural networks on a wide range of datasets, including Cifar-10, Image Net, MNIST, and SVHN. |
| Dataset Splits | No | The paper mentions a training set and a test set, but does not explicitly provide details for a validation set split (e.g., percentages or sample counts). |
| Hardware Specification | Yes | For Cifar-10 we use one NVIDIA P100 GPU and for Image Net we use four NVIDIA P100 GPUs. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with their specific versions). |
| Experiment Setup | Yes | For Cifar-10, MNIST and SVHN, we train the models for 440 epochs using our proposed algorithm. The learning rate is initialized to be 0.01, and is decayed by 0.1 at the 200th/400th epoch. For all the models, we use k = 16, γ = 8.0 and β = 16.0. |