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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Frequency-Masked Embedding Inference: A Non-Contrastive Approach for Time Series Representation Learning
Authors: En Fu, Yanyan Hu
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on 8 widely used time series datasets for classification and regression tasks, using linear evaluation and end-to-end fine-tuning, show that FEI significantly outperforms existing contrastive-based methods in terms of generalization. |
| Researcher Affiliation | Academia | En Fu1, Yanyan Hu1,2,* 1School of Intelligence Science and Technology, University of Science and Technology Beijing 2Institute of Artificial Intelligence, University of Science and Technology Beijing EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methods and processes using mathematical equations and descriptive text but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code/Appendix https://github.com/USTBInnovationPark/Frequencymasked-Embedding-Inference |
| Open Datasets | Yes | The dataset used in the experimental section is shown in Table 1. For the pre-training phase, we use the commonly utilized SLeep EEG dataset, which provides ample samples and is widely used for pre-training in various transfer learning methods (Zhang et al. 2022; Dong et al. 2023). For the downstream tasks, 8 publicly available datasets are utilized: 1) Gesture (Liu et al. 2009), 2) FD-B (Lessmeier et al. 2016), 3) EMG (Goldberger et al. 2000), 4) EPI(Andrzejak et al. 2001), 5) HAR (Anguita et al. 2013), 6) 128 UCR (Dau et al. 2018), 7) C-MAPSS (Saxena et al. 2008), and 8) Bearing (Wang et al. 2018). The UCR Time Series Classification Archive. https://www.cs.ucr.edu/ eamonn/time series data 2018/. Accessed: 2024-08-01. |
| Dataset Splits | Yes | The model with the lowest validation loss is used as the final test model by early stopping for all datasets except for the 128 UCR dataset, which does not have a validation set division, so all models are tested directly after training ends. |
| Hardware Specification | No | The paper discusses model training and evaluation but does not specify any particular hardware (e.g., GPU models, CPU types) used for the experiments. |
| Software Dependencies | No | The paper mentions using '1D Res Net' as the encoder network but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | In the linear evaluation process, we freeze the encoder, and optimize only a linear classifier, with a maximum training iteration of 300 and an initial learning rate of 1e-4. In the end-to-end fine-tuning process, both the encoder and the classifier are optimized, with a maximum iteration of 100 and a smaller learning rate of 1e-5 to prevent overfitting. The basic setup of proposed FEI for the pre-training process follows Sim MTM and TF-C. Additional configurations can be found in the Appendix. |