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https://github.com/pydata/xarray/issues/3232#issuecomment-524420000 https://api.github.com/repos/pydata/xarray/issues/3232 524420000 MDEyOklzc3VlQ29tbWVudDUyNDQyMDAwMA== 1217238 2019-08-23T18:38:19Z 2019-08-23T18:38:19Z MEMBER

I have not thought too much about these yet. But I agree that they will probably require backend specific logic to do efficiently.

On Fri, Aug 23, 2019 at 12:13 PM firdaus janoos notifications@github.com wrote:

While it is pretty straightforward to implement a lot of standard xarray operations with a pytorch / Jax backend (since they just fallback on native functions) - it will be interesting to think about how to implement rolling operations / expanding / exponential window in a way that is both efficient and maintains differentiability.

Expanding and exponential window operations would be easy to do leveraging RNN semantics - but doing rolling using convolutions is going to be very inefficient.

Do you have any thoughts on this?

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