GRU-ODE-Bayes: Continuous Modeling of Sporadically-Observed Time Series
Authors: Edward De Brouwer, Jaak Simm, Adam Arany, Yves Moreau
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additionally, empirical evaluation shows that our method outperforms the state of the art on both synthetic data and real-world data with applications in healthcare and climate forecast. |
| Researcher Affiliation | Academia | Edward De Brouwer ESAT-STADIUS KU LEUVEN Leuven, 3001, Belgium edward.debrouwer@esat.kuleuven.be |
| Pseudocode | Yes | Algorithm 1 GRU-ODE-Bayes |
| Open Source Code | Yes | Code is available in the following anonymous repository : https://github.com/edebrouwer/gru_ ode_bayes |
| Open Datasets | Yes | We use the publicly available MIMIC-III clinical database (Johnson et al., 2016)... We use the publicly available United State Historical Climatology Network (USHCN) daily data set (Menne et al.) |
| Dataset Splits | Yes | We report the performance using 5-fold cross-validation. Hyperparameters (dropout and weight decay) are chosen using an inner holdout validation set (20%) and performance are assessed on a left-out test set (10%). |
| Hardware Specification | Yes | We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | Hyperparameters (dropout and weight decay) are chosen using an inner holdout validation set (20%) and performance are assessed on a left-out test set (10%). Those folds are reused for each model we evaluated for sake of reproducibility and fair comparison (More details in Appendix O). |