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Found 5 papers, 5 papers with code
EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs
We show that our semantic subsequence preserving improves the existing masking methods in self-prediction literature and find that preserving 50\% of EEG recordings will result in the most accurate results on all 6 tasks on average.
Series2Vec: Similarity-based Self-supervised Representation Learning for Time Series Classification
Our evaluation of Series2Vec on nine large real-world datasets, along with the UCR/UEA archive, shows enhanced performance compared to current state-of-the-art self-supervised techniques for time series.
Improving Position Encoding of Transformers for Multivariate Time Series Classification
We then proposed a new absolute position encoding method dedicated to time series data called time Absolute Position Encoding (tAPE).
Ranked #1 on
Time Series Classification
on Heartbeat
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks.
Disjoint-CNN for Multivariate Time Series Classification
Time series classification algorithms have been mainly dominated by non-deep learning models.
Ranked #1 on
Time Series Classification
on pendigits
