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Thank you very much for the package, it is great work
I am trying to use the `robust_pca` function on a large dataset and the computation is very slow. This issue seems to be referenced in #18, #36…
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I was wondering if there are any plans to make available means of memory management? It would be really helpful in debugging out of memory errors that pop up (I've been specifically running into this …
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When I try to use Tucker decomposition of a large sparse matrix, Tensorly crashes. I have used both the MXNet and NumPy backend, and both cause the crash due to memory issues.
The dimensions of my …
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Issue:
MP3 encoder does not seem to get picked up by ffmpeg:
```
scopatz@artemis ~ $ conda list lame
# packages in environment at /home/scopatz/miniconda:
#
# Name Versi…
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I tried to run Robust PCA on a torch array. The dimensions are around 500000*375. They array can perfectly fit on my GPU as I ran robust matrix decomposition without any issues. I am sure why robust P…
rtmlp updated
6 years ago
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Hi,
in tensorly.decomposition.parafac, it says that the error from each iteration is returned. Is there an inconsistency with the documentation or am I just unable to activate this return?
Thank…
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Hi,
It would be helpful if you added a license file, to communicate how you want to share this code.
Thanks!
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If I understand correctly, the variable names `core` and `factors` for the results returned by `tucker` and `non_negative_tucker` functions should be swapped here:
https://github.com/tensorly/tensorl…
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Would be nice to have a package for [`tensorly`]( https://github.com/tensorly/tensorly ).
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I'm trying to use `parafac` on a complex tensor. I can handle the real and imaginary parts separately by calling `PR = parafac(np.real(T), rank = k)` and `PI = parafac(np.imag(T), rank = k)` and then …