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As the topic suggests, I am highly interested to see if this library can support multivariable time series with channels that have different sampling rate and thus different length within a sample For instance,
From this paper, they implemented channel-independent path which are all concated at the end before MLP layers. The legacy tensorflow code is provided as follows:
Can We Ditch Feature Engineering? End-to-End DeepLearning for Affect Recognition from Physiological Sensor Data https://github.com/Emognition/dl-4-tsc
The text was updated successfully, but these errors were encountered:
As the topic suggests, I am highly interested to see if this library can support multivariable time series with channels that have different sampling rate and thus different length within a sample For instance,
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From this paper, they implemented channel-independent path which are all concated at the end before MLP layers. The legacy tensorflow code is provided as follows:
Can We Ditch Feature Engineering? End-to-End DeepLearning for Affect Recognition from Physiological Sensor Data https://github.com/Emognition/dl-4-tsc
The text was updated successfully, but these errors were encountered: