We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
In this followup paper the authors talk about using Sinkhorn distances to reduce complexity from O(dim ** 3 * log(dim)) to O(nnz ** 2).
O(dim ** 3 * log(dim))
O(nnz ** 2)
Does this library use this Sinkhorn trick? Or, if not, do you have any ideas on how it might compare to the SInkhorn trick in terms of runtime?
Thanks! ~ Ben
The text was updated successfully, but these errors were encountered:
The matlab code for the linked paper is here: https://github.com/gaohuang/S-WMD (although they don't specify a license).
I would definitely be interested in a python/OR-tools-based implementation
Sorry, something went wrong.
Whoa, I completely missed this! Thanks for the suggestions, I need to take a look.
I enjoyed this talk about sWMD: https://channel9.msdn.com/Events/Neural-Information-Processing-Systems-Conference/Neural-Information-Processing-Systems-Conference-NIPS-2016/Supervised-Word-Movers-Distance
I also came across this Python implementation of sWMD: https://github.com/ByronPan/sWMD
@vmarkovtsev, any updated thoughts about how much work it'd be to extend the approach taken here in wmd-relax?
No branches or pull requests
In this followup paper the authors talk about using Sinkhorn distances to reduce complexity from
O(dim ** 3 * log(dim))
toO(nnz ** 2)
.Does this library use this Sinkhorn trick? Or, if not, do you have any ideas on how it might compare to the SInkhorn trick in terms of runtime?
Thanks!
~ Ben
The text was updated successfully, but these errors were encountered: