Latent Ordinary Differential Equations for Irregularly-Sampled Time Series
Authors: Yulia Rubanova, Ricky T. Q. Chen, David K. Duvenaud
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 {rubanova, rtqichen, duvenaud}@cs.toronto.edu |
| 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. |