issue_comments: 524411995
This data as json
| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/issues/3232#issuecomment-524411995 | https://api.github.com/repos/pydata/xarray/issues/3232 | 524411995 | MDEyOklzc3VlQ29tbWVudDUyNDQxMTk5NQ== | 923438 | 2019-08-23T18:13:35Z | 2019-08-23T18:13:35Z | NONE | 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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