Modeling Trajectories with Neural Ordinary Differential Equations
Authors: Yuxuan Liang, Kun Ouyang, Hanshu Yan, Yiwei Wang, Zekun Tong, Roger Zimmermann
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the task of trajectory classification demonstrate the superiority of our framework against the RNN counterparts. |
| Researcher Affiliation | Academia | National University of Singapore, Singapore |
| Pseudocode | Yes | Algorithm 1: The ST-ODE model |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We conduct our experiments over two public datasets: Geo Life [Zheng et al., 2010]: ... Grab-Posisi [Huang et al., 2019]: |
| Dataset Splits | Yes | For both datasets, we partition the data into training, validation and test data by a ratio of 8:1:1. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | Yes | We implement Traj ODE and the baselines with Py Torch 1.7. |
| Experiment Setup | Yes | Our model is trained by an Adam optimizer with an initial learning rate of 0.01, reduced by 1/10 every 20 epochs. The batch size is 128 and 512 over the two datasets, respectively. For simplicity, we use the same hidden dimensionality at the encoder and decoder, and conduct a grid search for m from 16 to 512. The ODE solvers in both ST-ODE and CNF are the Euler Method, where the evaluation functions are 3-layer MLPs with m hidden units in each layer. The trade-off parameter (γ) in Eq. 10 is set as 5e 4. |