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..
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
Authors: Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, Marinka Zitnik
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the new method on eight datasets, including electrodiagnostic testing, human activity recognition, mechanical fault detection, and physical status monitoring. Experiments against eight state-of-the-art methods show that TF-C outperforms baselines by 15.4% (F1 score) on average in one-toone settings (e.g., fine-tuning an EEG-pretrained model on EMG data) and by 8.4% (precision) in challenging one-to-many settings (e.g., fine-tuning an EEG-pretrained model for either hand-gesture recognition or mechanical fault prediction), reflecting the breadth of scenarios that arise in real-world applications. |
| Researcher Affiliation | Collaboration | Xiang Zhang Harvard University EMAIL Ziyuan Zhao Harvard University EMAIL Theodoros Tsiligkaridis MIT Lincoln Laboratory EMAIL Marinka Zitnik Harvard University EMAIL |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. It describes the methods and loss functions in prose and mathematical notation. |
| Open Source Code | Yes | The source code and datasets are available at https://github.com/mims-harvard/TFC-pretraining. |
| Open Datasets | Yes | The source code and datasets are available at https://github.com/mims-harvard/TFC-pretraining. ... (1) SLEEPEEG [61] has 371,055 univariate brainwaves (EEG; 100 Hz) collected from 197 individuals. ... (8) EMG [66] consists of 163 EMG samples with 3-class labels implying muscular diseases. Dataset labels are not used in pre-training. Further dataset statistics are in Appendix D and Table 3. |
| Dataset Splits | Yes | We randomly split the dataset into 80% for training, 10% for validation, and 10% for testing. |
| Hardware Specification | Yes | Computational resources: All experiments were conducted on NVIDIA A100 GPUs. |
| Software Dependencies | Yes | We use PyTorch [73] with Python 3.8. |
| Experiment Setup | Yes | All models are trained for 100 epochs with Adam optimizer (Ī²1=0.9, Ī²2=0.999), a weight decay of 1eā4, and a batch size of 128. For fine-tuning, we use a learning rate of 1eā3 and train for 20 epochs. The learning rate for pre-training is 1eā4. We randomly split the dataset into 80% for training, 10% for validation, and 10% for testing. |