We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| White-Link/UnsupervisedScalableRepresentationLearningTimeSeries | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| load_UCR_dataset | Ran | White-Link/UnsupervisedScalableRepresentationLearningTimeSeries/ucr.py code served (permissive licence) · get_code("d15aece8986bdd69") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Time series constitute a challenging data type for machine learning algorithms, due to their highly variable lengths and sparse labeling in practice. In this paper, we tackle this challenge by proposing an unsupervised method to learn universal embeddings of time series. Unlike previous works, it is scalable with respect to their length and we demonstrate the quality, transferability and practicability of the learned representations with thorough experiments and comparisons. To this end, we combine an encoder based on causal dilated convolutions with a novel triplet loss employing time-based negative sampling, obtaining general-purpose representations for variable length and multivariate time series.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1901.10738")
get_code_for_paper("1901.10738")
have("1901.10738")
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