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..
Implicit Jacobian regularization weighted with impurity of probability output
Authors: Sungyoon Lee, Jinseong Park, Jaewook Lee
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also evaluate the Explicit Jacobian Regularization, which outperforms state-of-the-art sharpness-aware optimization methods, SAM (Foret et al., 2021) and ASAM (Kwon et al., 2021) (Table 1 in Section 5.3). |
| Researcher Affiliation | Academia | 1Department of Computer Science, Hanyang University 2Department of Industrial Engineering, Seoul National University. |
| Pseudocode | Yes | Algorithm 1 Power iteration; Algorithm 2 Power iteration for the Jacobian |
| Open Source Code | No | The paper provides links to third-party libraries and implementations used (e.g., VGG, ResNet, Py Hessian, SAM implementation), but does not provide a link or explicit statement for the open-sourcing of its own core methodology (EJR). |
| Open Datasets | Yes | We use the CIFAR-10 dataset ((Krizhevsky & Hinton, 2009), https://www.cs.toronto.edu/~kriz/cifar.html) and the MNIST dataset which have C = 10 number of classes. We also conduct some experiments on the CIFAR-100 dataset with the number of classes C = 100. |
| Dataset Splits | Yes | The paper uses standard benchmark datasets like CIFAR-10, CIFAR-100, and MNIST, which have predefined and commonly used training and testing splits. While explicit percentages are not stated, the use of these standard datasets implies well-defined splits. |
| Hardware Specification | No | The paper mentions running experiments but does not provide specific details about the hardware used, such as GPU models, CPU types, or memory configurations. |
| Software Dependencies | No | The paper mentions using 'Pytorch code' and a 'Py Hessian' tool, but it does not specify version numbers for these or any other software libraries or dependencies, which would be necessary for reproducibility. |
| Experiment Setup | Yes | Table 3 and Table 4 detail the experimental settings including 'Batch Size', 'Initial lr', 'Epochs', 'lr scheduler', 'Momentum', and 'Weight Decay' for various models and datasets. For example, for Simple CNN on CIFAR-10, 'Initial lr' values like 0.01, 0.03 are given. |