Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features
Authors: Thomas McDonald, Mauricio Álvarez
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide evidence that our model is capable of capturing highly nonlinear behaviour in real-world multivariate time series data. In addition, we find that our approach achieves comparable performance to a number of other probabilistic models on benchmark regression tasks. 5 Experiments |
| Researcher Affiliation | Academia | Thomas M. Mc Donald Department of Computer Science University of Sheffield tmmcdonald1@sheffield.ac.uk Mauricio A. Álvarez Department of Computer Science University of Sheffield mauricio.alvarez@sheffield.ac.uk |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available in the repository: https://github.com/tomcdonald/Deep-LFM. The code was also included within the supplemental material at the time of review. |
| Open Datasets | Yes | We evaluate its performance on a subset of the CHARIS dataset (ODC-BY 1.0 License) [Kim et al., 2016], which can be found on the Physio Net data repository [Goldberger et al., 2000]. Finally, we also evaluated the performance of the model on two regression datasets from the UCI Machine Learning Repository [Dua and Graff, 2017], Powerplant and Protein |
| Dataset Splits | Yes | we focus here on the more challenging task of extrapolating beyond the training input-space by training the aforementioned models on the first 700 observations and withholding the remaining 300 as a test set. Progression of validation set metrics on the UCI benchmarks, averaged over three folds. |
| Hardware Specification | Yes | All of the experimental results in this section were obtained using a single node of a cluster, consisting of a 40 core Intel Xeon Gold 6138 CPU and a NVIDIA Tesla V100 SXM2 GPU with 32GB of RAM. |
| Software Dependencies | No | The paper mentions 'pure Py Torch' but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Unless otherwise specified, all models in this section were implemented in pure Py Torch, trained using the Adam W optimizer with a learning rate of 0.01 and a batch size of 1000. The DLFMs and DGPs with random feature expansions [Cutajar et al., 2017] tested all utilised a single hidden layer of dimensionality DF (ℓ) = 3, 100 Monte Carlo samples and 100 random features per layer. |