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..
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. |