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 [1].
Overcoming Forgetting in Fine-Grained Urban Flow Inference via Adaptive Knowledge Replay
Authors: Haoyang Yu, Xovee Xu, Ting Zhong, Fan Zhou
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four large-scale real-world FUFI datasets demonstrate that our proposed model consistently outperforms strong baselines and effectively mitigates the forgetting problem. |
| Researcher Affiliation | Academia | University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Adaptive Knowledge Replay (AKR) |
| Open Source Code | Yes | Source code is available at: https://github.com/Patton Yu/CUFAR. |
| Open Datasets | Yes | Experiments are conducted on four real-world taxi traffic datasets collected continuously for four years (2013 to 2016) in Beijing (Liang et al. 2019). We denote the four datasets as Taxi BJ Task-1 to Task-4. |
| Dataset Splits | No | The paper mentions 'validation losses' and 'validation set' implicitly but does not provide specific details about the training/validation/test dataset splits (e.g., percentages, sample counts, or explicit splitting methodology). |
| Hardware Specification | Yes | All experiments are conducted on RTX 3090 with PyTorch. |
| Software Dependencies | No | The paper mentions 'PyTorch' but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | The optimizer is Adam, learning rate is 1e 4, filter size F is 128, temporal conv layers K is 15 (hourly from 9AM to 12PM), memory buffer size S is 1,000, batch B and BM s sizes are 16 and 2, respectively. The resolution of the flow map Xfg is 128x128, the upscaling factor N is 4. |