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Found 2 papers, 1 papers with code
Deep Learning for Time Series Classification of Parkinson's Disease Eye Tracking Data
In the present work, we investigate the use of state-of-the-art deep learning algorithms to perform Parkinson's disease classification using eye-tracking data from saccade experiments.
Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels
When applied to the largest binary UCR dataset, Detach-ROCKET is able to improve test accuracy by $0. 6\%$ while reducing the number of features by $98. 9\%$.
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