Utilizing Expert Features for Contrastive Learning of Time-Series Representations
Authors: Manuel T Nonnenmacher, Lukas Oldenburg, Ingo Steinwart, David Reeb
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate on three real-world time-series datasets that Exp CLR surpasses several state-of-the-art methods for both unsupervised and semi-supervised representation learning. |
| Researcher Affiliation | Collaboration | 1Bosch Center for Artificial Intelligence (BCAI), Robert Bosch Gmb H, Renningen, Germany 2Institute for Stochastics and Applications, University of Stuttgart, Stuttgart, Germany. |
| Pseudocode | No | The paper contains mathematical formulations and descriptions of algorithms but does not include any explicitly labeled pseudocode blocks or algorithm listings. |
| Open Source Code | Yes | Py Torch code implementing our method is provided at https://github.com/boschresearch/expclr. |
| Open Datasets | Yes | Human Activity Recognition (HAR): The HAR dataset (Cruciani et al., 2019)... We downloaded the dataset from the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones)... Sleep Stage Classification (Sleep EDF): The dataset originates from (Goldberger et al., 2000; Kemp et al., 2000)... We downloaded the dataset from the Pyhsio Net database (https://physionet.org/content/sleep-edf/1.0.0)... MIT-BIH Atrial Fibrillation (Waveform): This dataset (Goldberger et al., 2000)... We downloaded the data from the Pyhsio Net database (https://physionet.org/content/afdb/1.0.0)... |
| Dataset Splits | Yes | While for hyperparameter optimization we split the training set X into 80% training and 20% validation data, for our comparisons experiments we make use of the full training set and evaluate the representations on the test set. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch code implementing our method' but does not specify the version of PyTorch or any other software dependencies with version numbers. |
| Experiment Setup | Yes | To capture relevant temporal properties and to improve training stability (Bai et al., 2018), we choose as a base encoder temporal convolutional network (TCN) (Lea et al., 2017) layers in a Res Net (He et al., 2016) architecture with eight such temporal blocks. For the optimization step we used the Adam optimizer with parameters β1 = 0.9, β2 = 0.999 and exponential decay γ = 0.99. To enable a fair comparison between Exp CLR and the competing methods, we optimize the learning rate for each method and dataset individually via a grid search and identify τ = 1, = 1 (Eq. 4), embedding dimension e = 100 and batch size of 64 as a good compromise over all datasets and algorithms. |