Segmenting Hybrid Trajectories using Latent ODEs
Authors: Ruian Shi, Quaid Morris
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here we investigate the Lat Seg ODE s ability to simultaneously perform accurate reconstruction and segmentation on synthetic and semi-synthetic data sets. |
| Researcher Affiliation | Collaboration | 1University of Toronto 2Vector Institute, Toronto 3Memorial Sloan Kettering Cancer Center. |
| Pseudocode | Yes | The full algorithm is provided in Appendix A. |
| Open Source Code | Yes | An implementation is available at: https://github. com/Ian Shi1996/Latent Segmented ODE. |
| Open Datasets | Yes | UCI Character Trajectories data set (Dua & Graff, 2017). |
| Dataset Splits | Yes | We hold out 300 validation trajectories, 150 test trajectories, and train the Lat Seg ODE base model on the SDFs contained in the remaining trajectories. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of the 'ruptures library' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | Data parameters, model architecture and hyper-parameters are reported in Appendix E. |