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..
Random Function Descent
Authors: Felix Benning, Leif Döring
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 7 We conduct a case study on the MNIST dataset. In Figure 3, RFD is benchmarked against step size tuned Adam [29] and stochastic gradient descent (SGD). |
| Researcher Affiliation | Academia | Felix Benning University of Mannheim EMAIL Leif Döring University of Mannheim EMAIL |
| Pseudocode | No | The paper provides mathematical definitions and properties of RFD but does not include a formal pseudocode block or algorithm. |
| Open Source Code | Yes | Code availability: Our implementation of RFD can be found at https://github.com/ Felix Benning/pyrfd and the package can also be installed from Py PI via pip install pyrfd . |
| Open Datasets | Yes | For our case study we use the negative log likelihood loss to train a neural network [3, M7] on the MNIST dataset [34]. We also trained a different model (M5 [3]) on the Fashion MNIST dataset [56]. |
| Dataset Splits | No | The paper mentions 'Validation loss' and 'test data set' being used for tuning, but does not provide specific train/validation/test split percentages or counts for the datasets. |
| Hardware Specification | No | The Experiments in this work were partially carried out on the compute cluster of the state of Baden-Würtemberg (bw HPC). |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies used in the experiments (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For our case study we use the negative log likelihood loss to train a neural network [3, M7] on the MNIST dataset [34]. Training on the MNIST dataset (batch size 1024). We also trained a different model (M5 [3]) on the Fashion MNIST dataset [56] with batch size 128. RFD is benchmarked against step size tuned Adam [29] and stochastic gradient descent (SGD). |