Comments (1)
also
On the other hand, CuPy uses a more standard, non-batched routine for SVD, which is less likely to run into memory issues for large matrices. Also, CuPy's SVD returns singular values in descending order, which is the typical behavior expected by users.
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Related Issues (20)
- Appendix 6: Rederive Tanaka from scratch
- bad issue
- Rename SVDSmoothing to TruncatedSVD model everywhere
- Need to say something about the MLP3 model in the matgen section HOT 2
- Selective plotting HOT 1
- bug calling TPL
- update the docs on rescale_eigenvalues
- change detX constraint to use rescale_eigenvalues
- Any plans to support pytorch2.x? HOT 2
- Add ability to read layer_map for pytorch model bin files
- Bug in reading some models off the file system
- getWeights() can not pull all layer ids
- add torch.low_rank() option
- Can weightwatcher still be useful for model with RNN layers?
- add inverse option HOT 1
- WeightWatcher.ipynb 'numpy.ndarray' object has no attribute 'extend' HOT 1
- using alpha monitor the training process of the model HOT 5
- What does the layer_id refer to in the details df ?
- Add support for Keras Bidirecitonal layers
- LSA Example notebook does not open google colab HOT 1
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