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..
GRU-ODE-Bayes: Continuous Modeling of Sporadically-Observed Time Series
Authors: Edward De Brouwer, Jaak Simm, Adam Arany, Yves Moreau
NeurIPS 2019 | Venue PDF | 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 EMAIL |
| 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). |