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I am trying to estimate ECG metrics with a combination of Conv-layers and CFCs, taking inspiration from the Atari tutorial. Unlike Atari, where convolutions extract features from images, in my case, convolutions work on the time dimension of the ECG signal itself. Therefore, the time dimension is modified by the convolutions. Will this have an impact on the performance of the CFC?
Also, if you can comment on the implementation of the CFC, that will be very helpful for understanding what each input to the CFC function does. As of now, tutorials are very helpful in showcasing the potential of LTCs and CFCs, but stronger documentation is needed for development.
Can you also share a sample code you used for generating gradient maps for the CFCs, as highlighted in the paper?
Thank you very much for your time!
The text was updated successfully, but these errors were encountered:
Hi,
I am trying to estimate ECG metrics with a combination of Conv-layers and CFCs, taking inspiration from the Atari tutorial. Unlike Atari, where convolutions extract features from images, in my case, convolutions work on the time dimension of the ECG signal itself. Therefore, the time dimension is modified by the convolutions. Will this have an impact on the performance of the CFC?
Also, if you can comment on the implementation of the CFC, that will be very helpful for understanding what each input to the CFC function does. As of now, tutorials are very helpful in showcasing the potential of LTCs and CFCs, but stronger documentation is needed for development.
Can you also share a sample code you used for generating gradient maps for the CFCs, as highlighted in the paper?
Thank you very much for your time!
The text was updated successfully, but these errors were encountered: