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 [1].
Neural Rough Differential Equations for Long Time Series
Authors: James Morrill, Cristopher Salvi, Patrick Kidger, James Foster
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run experiments applying Neural RDEs to four real-world datasets. For each model, and each hyperparameter combination, we run the experiment three times and report the mean and standard deviation of the test metrics. We additionally report mean training times and memory usages. |
| Researcher Affiliation | Academia | 1Mathematical Institute, University of Oxford, UK 2The Alan Turing Institute, British Library, UK. Correspondence to: James Morrill <EMAIL>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We make this approach available in the [redacted] open source project. |
| Open Datasets | Yes | Our ๏ฌrst example uses the Eigen Worms dataset from the UEA archive from Bagnall et al. (2017). Next we consider three separate problems, using data from the TSR archive (Tan et al., 2020), coming originally from the Beth Israel Deaconess Medical Centre (BIDMC). |
| Dataset Splits | No | In practice, when choosing a ๏ฌnal model, one would choose that with depth and step values that minimise the validation loss, as in any hyperparamter value selection. However, specific dataset split percentages for training, validation, and test sets are not provided in the main text. |
| Hardware Specification | No | The paper mentions 'GPU memory' in relation to memory usage limits, but does not provide specific details about the type or model of GPU, CPU, or any other hardware component used for the experiments. |
| Software Dependencies | No | The paper mentions the use of 'Signatory (Kidger & Lyons, 2020b)' and 'torchdiffeq (Chen, 2018)', but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | Recall that the Neural RDE approach features two hyperparameters, corresponding to log-signature depth and step size. Accordingly for every experiment we run Neural RDEs for all depths in N = 2, 3 and all step sizes in 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024. |