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 [1].
The Crucial Role of Normalization in Sharpness-Aware Minimization
Authors: Yan Dai, Kwangjun Ahn, Suvrit Sra
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our conclusions are backed by various experiments. |
| Researcher Affiliation | Academia | Yan Dai IIIS, Tsinghua University EMAIL Kwangjun Ahn EECS, MIT EMAIL Suvrit Sra TU Munich / MIT EMAIL |
| Pseudocode | No | The paper contains mathematical equations and derivations but no structured pseudocode or algorithm blocks are explicitly presented or labeled. |
| Open Source Code | No | The paper does not contain an explicit statement indicating the release of source code for the described methodology, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | We train a ResNet18 on the CIFAR-10 dataset |
| Dataset Splits | No | The paper mentions training on CIFAR-10 and using specific initializations/checkpoints, but it does not explicitly state the dataset splits (e.g., percentages or counts for training, validation, or testing sets) used for reproduction. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for its experiments, such as specific GPU or CPU models, or details about the computing environment. |
| Software Dependencies | No | The paper mentions deep learning models and optimizers but does not provide specific software dependency details, such as library names with version numbers (e.g., 'PyTorch 1.9', 'Python 3.8') used for the experiments. |
| Experiment Setup | Yes | Figure 5: Training ResNet18 on CIFAR-10 from a bad global minimum (η = 0.001, batch size = 128). |