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.
Shedding Light on Time Series Classification using Interpretability Gated Networks
Authors: Yunshi Wen, Tengfei Ma, Ronny Luss, Debarun Bhattacharjya, Achille Fokoue, Anak Agung Julius
ICLR 2025 | Venue PDF | LLM Run Details | Input Tokens: 31,752 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,762 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 | Our proposed model achieves comparable performance with state-of-the-art deep learning models while additionally providing interpretable classifiers for various benchmark datasets. We further demonstrate that Interp GN outperforms state-of-the-art methods on the UEA multivariate TS classification archive (Bagnall et al., 2018), illustrate the interpretability of Interp GN on multivariate TS classification datasets, and finally apply our framework on a real-world healthcare dataset, MIMIC-III (Johnson et al., 2016). |
| Researcher Affiliation | Collaboration | 1Rensselaer Polytechnic Institute, 2Stony Brook University, 3IBM Research EMAIL, EMAIL EMAIL EMAIL |
| Pseudocode | No | The paper describes the methodology using mathematical formulations (e.g., Equation 1, 2) and provides code snippets for implementation details in the appendix, but it does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block in the main text. |
| Open Source Code | Yes | Code is available at: https://github.com/Yunshi Wen/Interpret Gated Network. |
| Open Datasets | Yes | We further demonstrate that Interp GN outperforms state-of-the-art methods on the UEA multivariate TS classification archive (Bagnall et al., 2018), illustrate the interpretability of Interp GN on multivariate TS classification datasets, and finally apply our framework on a real-world healthcare dataset, MIMIC-III (Johnson et al., 2016). |
| Dataset Splits | Yes | For each dataset, we train the models on the default training split with 5 different random seeds and report the average accuracy on the test split. ... For predicting in-hospital mortality, the data is highly imbalance, with more than 80% positive samples (patient survived). Therefore we randomly select a subset of 1500 positive and 1500 negative samples to evaluate our models. A sample in the dataset is a TS with M = 9 and T = 48. We further divide the subset into 80% training and 20% validation to evaluate the performance using 5-fold cross validation. |
| Hardware Specification | Yes | All the experiments run on a NVIDIA V100-SXM2-32GB GPUs. |
| Software Dependencies | Yes | All the experiments are implemented using Python 3.11 and Py Torch 2.4.0. |
| Experiment Setup | Yes | The default hyperparameter to produce results in Table 1 and Table 5 are summarized in Table3. For each dataset, we train the models on the default training split with 5 different random seeds and report the average accuracy on the test split. |