Neural Controlled Differential Equations for Irregular Time Series
Authors: Patrick Kidger, James Morrill, James Foster, Terry Lyons
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that our model achieves state-of-the-art performance against similar (ODE or RNN based) models in empirical studies on a range of datasets. Finally we provide theoretical results demonstrating universal approximation, and that our model subsumes alternative ODE models. |
| Researcher Affiliation | Academia | Patrick Kidger James Morrill James Foster Terry Lyons Mathematical Institute, University of Oxford The Alan Turing Institute, British Library {kidger, morrill, foster, tlyons}@maths.ox.ac.uk |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/patrick-kidger/Neural CDE. We have also released a library torchcde, at https://github.com/patrick-kidger/torchcde |
| Open Datasets | Yes | We consider the Character Trajectories dataset from the UEA time series classification archive [31]. We use data from the Physio Net 2019 challenge on sepsis prediction [32, 33]. We used the Speech Commands dataset [34]. |
| Dataset Splits | Yes | Appendix D.2 Character Trajectories: We use a 70%/10%/20% train/validation/test split. Appendix D.3 PhysioNet sepsis prediction: We use a 70%/10%/20% train/validation/test split for this data, as with Character Trajectories. Appendix D.4 Speech Commands: We use the recommended 80%/10%/10% train/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for running its experiments. |
| Software Dependencies | No | In our experiments, we were able to straightforwardly use the already-existing torchdiffeq package [24] without modification. The implementation via torchdiffeq is in Python. The paper mentions software packages like 'torchdiffeq' and 'Python' but does not specify their version numbers. |
| Experiment Setup | Yes | For every problem, the hyperparameters were chosen by performing a grid search to optimise the performance of the baseline ODE-RNN model. Equivalent hyperparameters were then used for every other model, adjusted slightly so that every model has a comparable number of parameters. Precise experimental details may be found in Appendix D, regarding normalisation, architectures, activation functions, optimisation, hyperparameters, regularisation, and so on. |