Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Disentangling Domain and General Representations for Time Series Classification
Authors: Youmin Chen, Xinyu Yan, Yang Yang, Jianfeng Zhang, Jing Zhang, Lujia Pan, Juren Li
IJCAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 20,355 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,216 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two public datasets and three real-world applications demonstrate the effectiveness of the proposed model against several stateof-the-art baselines. |
| Researcher Affiliation | Collaboration | Youmin Chen1 , Xinyu Yan1 , Yang Yang1 , Jianfeng Zhang2 , Jing Zhang3 , Lujia Pan2 and Juren Li1 1Zhejiang University 2Huawei Noah s Ark Lab 3Renmin University of China |
| Pseudocode | No | The paper states 'The whole learning algorithm of CADT is presented in the appendix.' but the appendix itself is not included in the provided text. |
| Open Source Code | Yes | The code and supplementary materials are available at https: //github.com/IJCAI-CADT/cadt |
| Open Datasets | Yes | In our experiments, we utilize five datasets: CHARGE and ELEC from industrial practice, and UCIHAR [Reyes-Ortiz et al., 2016], WISDM [Kwapisz et al., 2011], Sleep EDF [Ragab and Eldele, 2022] from publicly available datasets. |
| Dataset Splits | No | The paper mentions training and target samples but does not provide specific training/validation/test dataset splits (e.g., percentages or counts) or reference predefined splits within the provided text. |
| Hardware Specification | No | The paper states 'The implementation detailed will be presented in the supplementary materials.' and does not include specific hardware details such as GPU/CPU models or memory in the provided text. |
| Software Dependencies | No | The paper states 'The implementation detailed will be presented in the supplementary materials.' and does not include specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) in the provided text. |
| Experiment Setup | No | The paper states 'The implementation detailed will be presented in the supplementary materials.' and does not include specific experimental setup details like hyperparameter values or training configurations in the provided text. |