Comments (2)
JeanKossaifi
commented on June 23, 2024
1
@SCIKings TensorLy-Torch provides pytorch based layers and factorizations on top of TensorLy. You can have a look at the hooks we provide (e.g. tensor dropout), you can write a similar hook for the activations you are interested in.
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yngvem
commented on June 23, 2024
Closing as this is not an issue with TensorLy.
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Related Issues (20)
- All nan in matrix come from non negative tucker decomposition HOT 2
- Init mode == "random" does not return the correct shape in initialize_tucker HOT 3
- It appears that partial_unfold works using sparse tensors, but it is not clear in the documentation
- Better random init of factorized tensors HOT 1
- svd_interface will throw an error if the number of rows of the matrix is smaller than it's columns HOT 1
- numpy.core._exceptions._ArrayMemoryError HOT 2
- Is there any t-product implementation code in tensorly?Thanks HOT 1
- More descriptive message when random PARAFAC2 rank is infeasible given shape HOT 1
- AssertionError: `tensorly.tt_tensor.validate_tt_rank` test HOT 1
- Randomised_CP function throws a Singular Matrix error HOT 2
- Tensor Conversion in TensorLy Does Not Preserve PyTorch Tensor dtype and device Attributes
- PARAFAC2 for missing data HOT 1
- Panel Dataset Time, Company, Feature HOT 1
- No attribute "device" when using Numpy backend HOT 3
- Remove MXNet from doc
- Parafac\parafac2 projection HOT 1
- Tensorly support float16/int8 HOT 1
- OOM issue HOT 6
- Question: CP decomposition of symmetric tensor with repeating factor matrix HOT 1
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