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..
Explainable Origin-Destination Crowd Flow Interpolation via Variational Multi-Modal Recurrent Graph Auto-Encoder
Authors: Qiang Zhou, Xinjiang Lu, Jingjing Gu, Zhe Zheng, Bo Jin, Jingbo Zhou
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two real-world datasets validate that VMR-GAE outperforms the state-of-the-art baselines. Also, an exploratory empirical study shows that the proposed explainer can generate meaningful spatiotemporal explanations. |
| Researcher Affiliation | Collaboration | 1Nanjing University of Aeronautics and Astronautics, Nanjing, China 2Baidu Research, Beijing, China 3Dalian University of Technology, Dalian, China |
| Pseudocode | No | The paper describes algorithms in prose (e.g., efficient explanation generation algorithm) but does not provide structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing their code for the described work, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | NYC dataset was generated from NYC taxicab data published by NYC TLC1. 1https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page |
| Dataset Splits | Yes | Then, we perform a random 7:1:2 division to create the training, validation, and test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper discusses the models and architectures used but does not provide specific ancillary software details, such as library names with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The unit time step is 1h in Beijing and 2h in NYC datasets. We choose the number of input snapshots T as 6 and set the diffusion step to be 2 in this work. |