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..
Memorization With Neural Nets: Going Beyond the Worst Case
Authors: Sjoerd Dirksen, Patrick Finke, Martin Genzel
JMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify our theoretical result with numerical experiments and additionally investigate the effectiveness of the algorithm on MNIST and CIFAR-10. |
| Researcher Affiliation | Collaboration | Sjoerd Dirksen EMAIL Mathematical Institute Utrecht University 3584 CD Utrecht, Netherlands; Patrick Finke EMAIL Mathematical Institute Utrecht University 3584 CD Utrecht, Netherlands; Martin Genzel EMAIL Merantix Momentum GmbH 13355 Berlin, Germany |
| Pseudocode | Yes | Algorithm 1 Interpolation; Algorithm 2 Interpolation (experiments) |
| Open Source Code | Yes | Code is available at https://github.com/patrickfinke/memo. We use Python 3, Scikit-learn, and Num Py. |
| Open Datasets | Yes | we additionally investigate the effectiveness of the algorithm on MNIST and CIFAR-10. Examining binary classification subproblems of the MNIST data set (Le Cun et al., 1998) |
| Dataset Splits | No | The paper describes experiments on various datasets (Two Moons, MNIST, CIFAR-10) but does not specify explicit training/test/validation splits or methodologies used for splitting the data for its experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | Code is available at https://github.com/patrickfinke/memo. We use Python 3, Scikit-learn, and Num Py. The paper mentions software dependencies like Python 3, Scikit-learn, and NumPy, but it only provides a version number for Python. Specific version numbers are missing for Scikit-learn and NumPy. |
| Experiment Setup | No | The paper describes the algorithms and their performance on datasets but does not explicitly provide details about hyperparameters (e.g., learning rate, batch size, epochs, optimizers) or system-level training settings for the numerical experiments. |