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..
Log Neural Controlled Differential Equations: The Lie Brackets Make A Difference
Authors: Benjamin Walker, Andrew Donald Mcleod, Tiexin Qin, Yichuan Cheng, Haoliang Li, Terry Lyons
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Log-NCDEs are shown to outperform NCDEs, NRDEs, the linear recurrent unit, S5, and MAMBA on a range of multivariate time series datasets with up to 50,000 observations. |
| Researcher Affiliation | Academia | 1Mathematical Institute, University of Oxford, UK 2Department of Electrical Engineering, City University of Hong Kong. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1https://github.com/Benjamin-Walker/ Log-Neural-CDEs |
| Open Datasets | Yes | We construct a toy dataset of 100,000 time series with 6 channels and 100 regularly spaced samples each. |
| Dataset Splits | Yes | Following Morrill et al. (2021), the original train and test cases are combined and resplit into new random train, validation, and test cases using a 70 : 15 : 15 split. |
| Hardware Specification | Yes | In order to compare the models, 1000 steps of training were run on an NVIDIA RTX 4090 with each model using the hyperparameters obtained from the hyperparameter optimisation. |
| Software Dependencies | No | The paper mentions 'Jax s vmap' and 'Adam' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | On the toy dataset, all models use a hidden state of dimension 64 and Adam with a learning rate of 0.0001 (Kingma & Ba, 2017). Full details on the hyperparameter grid search are in Appendix C.4. |