Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series
Authors: Yulia Rubanova, Ricky T. Q. Chen, David K. Duvenaud
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,440 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,271 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show experimentally that these ODE-based models outperform their RNN-based counterparts on irregularly-sampled data. 4 Experiments 4.1 Toy dataset 4.2 Quantitative Evaluation 4.3 Mu Jo Co Physics Simulation 4.4 Physionet 4.5 Human Activity dataset |
| Researcher Affiliation | Academia | Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud University of Toronto and the Vector Institute EMAIL |
| Pseudocode | Yes | Algorithm 1 The ODE-RNN. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We evaluated our model on the Physio Net Challenge 2012 dataset [Silva et al., 2012] |
| Dataset Splits | Yes | On each dataset, we used 80% for training and 20% for test. |
| Hardware Specification | No | The paper states: 'We thank the Vector Institute for providing computational resources.' However, this does not provide specific hardware details (e.g., CPU/GPU models, memory, cluster specifications). |
| Software Dependencies | No | The paper mentions using GRU, but does not provide specific version numbers for any software dependencies (e.g., Python, PyTorch, TensorFlow, specific ODE solvers). |
| Experiment Setup | No | The paper refers to supplementary material for details: 'See supplement for more details on hyperparameters.' The main text does not contain specific experimental setup details like hyperparameter values or training configurations. |