Random Function Descent
Authors: Felix Benning, Leif Döring
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 felix.benning@uni-mannheim.de Leif Döring University of Mannheim leif.doering@uni-mannheim.de |
| 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). |