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..
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion
Authors: Ling Yang, Shenda Hong
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Specifically, we firstly conducts downstream evaluations on three major tasks for time series including classification, forecasting and anomaly detection. Experimental results shows that our BTSF consistently significantly outperforms the state-of-the-art methods. |
| Researcher Affiliation | Academia | 1National Institute of Health Data Science, Peking University, Beijing, China 2Institute of Medical Technology, Health Science Center of Peking University, Beijing, China. |
| Pseudocode | No | The paper describes the method and its components mathematically and textually but does not include a formally labeled pseudocode block or algorithm. |
| Open Source Code | No | The paper does not provide a link or an explicit statement about the availability of its source code. It only mentions implementing existing works using public codes. |
| Open Datasets | Yes | We evaluate our learned representation on downstream classification tasks for time series on widely-used time series classification datasets (Anguita et al., 2013; Goldberger et al., 2000; Andrzejak et al., 2001; Moody, 1983). |
| Dataset Splits | Yes | In the training stage, we keep the original train/test splits of datasets and use the training set to train all the models. |
| Hardware Specification | Yes | In all experiments, we use Pytorch 1.8.1 (Paszke et al., 2017) and train all the models on a Ge Force RTX 2080 Ti GPU with CUDA 10.2. |
| Software Dependencies | Yes | In all experiments, we use Pytorch 1.8.1 (Paszke et al., 2017) and train all the models on a Ge Force RTX 2080 Ti GPU with CUDA 10.2. |
| Experiment Setup | Yes | We apply an Adam optimizer (Kingma & Ba, 2017) with a learning rate of 3e-4, weight decay of 1e-4 and batch size is set to 256. ... the dropout rate, temperature number τ and the loops number of iterative bilinear fusion. Table 6 illustrates that when the rate is set to 0.1 ... Table 7 demonstrates that when τ is set to 0.05 |