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..
SAM operates far from home: eigenvalue regularization as a dynamical phenomenon
Authors: Atish Agarwala, Yann Dauphin
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then present experimental results on realistic models which show: The SAM-EOS predicts the largest eigenvalue for Wide Resnet 28-10 on CIFAR10. 3. Experiments on basic models 4. Connection to realistic models |
| Researcher Affiliation | Industry | Atish Agarwala * 1 Yann Dauphin * 1 1Google Deep Mind. Correspondence to: Atish Agarwala <EMAIL>. |
| Pseudocode | No | The paper provides mathematical equations and theoretical derivations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the methodology or provide a link to a code repository. |
| Open Datasets | Yes | We conducted experiments on the popular CIFAR-10 dataset (Krizhevsky et al., 2009) using the Wide Resnet 28-10 architecture (Zagoruyko & Komodakis, 2016). |
| Dataset Splits | No | The paper mentions using CIFAR-10 and training on the 'first 2 classes of CIFAR' but does not specify the train/validation/test dataset splits (e.g., percentages, sample counts, or explicit standard split references beyond just the dataset name). |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU or CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks like PyTorch or TensorFlow with their versions). |
| Experiment Setup | Yes | For MSE, we use ฮท = 0.3, ยต = 0.005 and ฮท = 0.4, ยต = 0.005 for cross-entropy. We use the cosine learning rate schedule (Loshchilov & Hutter, 2016) and SGD instead of Nesterov momentum (Sutskever et al., 2013) to better match the theoretical setup. (...) We keep all other hyper-parameters to the default values described in the original Wide Resnet paper. |