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..
A New Attention Mechanism to Classify Multivariate Time Series
Authors: Yifan Hao, Huiping Cao
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | CA-SFCN is compared with 16 approaches using 14 different MTS datasets. The extensive experimental results show that the CA-SFCN outperforms state-of-the-art classification methods, and the cross attention mechanism achieves better performance than other attention mechanisms. |
| Researcher Affiliation | Academia | Yifan Hao and Huiping Cao New Mexico State University EMAIL |
| Pseudocode | No | The paper describes its approach using text and architectural diagrams (Figures 1 and 2) but does not provide a formal pseudocode or algorithm block. |
| Open Source Code | Yes | The source code can be found from https://github.com/huipingcao/nmsu yhao ijcai2020. |
| Open Datasets | Yes | 14 real-world datasets are used to test the performance of the proposed approaches [Dua and Graff, 2017; Karim et al., 2019] |
| Dataset Splits | No | The paper states that 14 real-world datasets are used and that the batch size for training is 128, but it does not specify the exact train/validation/test splits (percentages or counts) for these datasets. |
| Hardware Specification | Yes | All the methods are implemented using Python 3.7, and tested on a server with Intel Xeon Gold 5117 2.0G CPUs, 192GB RAM, and one Nvidia Tesla P100 GPU. |
| Software Dependencies | No | The paper mentions 'Python 3.7' and 'Tensor Flow' but does not provide a version number for TensorFlow, and only one software dependency (Python) has a specific version. |
| Experiment Setup | Yes | Adamoptimizer is used in the training process. The convolutional and pooling layers use the similar configuration as that in [Karim et al., 2019]. In particular, the convolutional layers contain three 2-D layers with filter sizes 8 1, 5 1, and 3 1, the corresponding filter numbers for the three layers are 128, 256, and 128. |