Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning
Authors: Kang Luo, Yuanshao Zhu, Wei Chen, Kun Wang, Zhengyang Zhou, Sijie Ruan, Yuxuan Liang
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two real-world datasets verify that Traj CL markedly enhances performance in trajectory classification tasks while showcasing superior generalization and interpretability. |
| Researcher Affiliation | Academia | 1Hong Kong University of Science and Technology (Guangzhou) 2University of Science and Technology of China 3Beijing Institute of Technology |
| Pseudocode | No | The paper describes modules and components but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete statement or link regarding the availability of its source code. |
| Open Datasets | Yes | We conduct extensive experiments on two real-world datasets Geo Life [Zheng et al., 2009] and Grab Posisi [Huang et al., 2019]. |
| Dataset Splits | Yes | These trajectories are subsequently partitioned in an 8:1:1 ratio for training, validation, and test data. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | Our model uses the Adam optimizer with the initial learning rate set to 0.001, reduced by 0.1 every 30 epochs. To avoid overfitting, we employ an early stopping with a patience of 20 epochs. The batch sizes for the Geo Life and Grab-Posisi datasets are 256 and 512. The default embedding dimensions are set to 64. For the predictor, we apply a 2-layer MLP uniformly. To initially merge local features from inputs, we employ two 3 × 1 convolutional layers. The weight parameters λ, φ, and η of the loss are 1, 0.5, and 0.5, respectively. |