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.
Modeling Trajectories with Neural Ordinary Differential Equations
Authors: Yuxuan Liang, Kun Ouyang, Hanshu Yan, Yiwei Wang, Zekun Tong, Roger Zimmermann
IJCAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 16,136 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,308 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 | 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. |