Comments (3)
Yeah the Fourier variant definitely sounds attractive.
I do have little time maintaining this right now (working on my PhD viva).
Also, this repo mostly serves as a playground to show the idea that KAN (in its current shape) can be reformulated into sth like MLP and become computationally efficient.
If you are going down on real experiments and have performance issues around all of this, please let me know and I'm willing to help.
In short: for now, I don't think I have the time to accept contributions; please go ahead fork the repo or copy code snippets under MIT license.
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Hey @Blealtan I am gonna roll my own version of this repo since it seems like you aren't actively replying.
I'll be pulling in the conv kan, fourier kan and doing comparisons on all of them.
Also, hoping to get into really doing break downs of why things are happening the way they are happening.
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Hi there @dschonholtz. Have you tried to measure the performance of ur algorithm compare to this one and the original?
You may refer to this tutorial
I'm looking forward to your results. Have a nice day!
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Related Issues (20)
- RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [800, 256]], which is output 0 of AsStridedBackward0, is at version 2; expected version 1 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True). HOT 1
- Difference in memory usage of mlp and kan HOT 1
- CUDA out of memory. HOT 2
- please report a bug to PyTorch. torch.linalg.lstsq: (Batch element 0): Argument 6 has illegal value. Most certainly there is a bug in the implementation calling the backend library. HOT 1
- AssertionError HOT 1
- Symbolic Formulation from efficient-KAN HOT 1
- Can configure efficient-kan model for continual learning? HOT 9
- Can still plot model and prune model in efficient kan? HOT 11
- Train Multi-KANs simultaneously! Coding realization HOT 2
- Layer fails on inputs that have been transposed previously HOT 1
- Something different from the official results for KAN. HOT 1
- ONNX
- How to pip install? HOT 3
- block: [0,0,0], thread: [1,0,0] Assertion `input_val >= zero && input_val <= one` failed. when using BCEloss
- which types of images could be better recognised using KAN approach?
- I would like to add a train test function to the KAN class
- flops and params
- Questions
- How to cite HOT 1
- Constant loss, network is not learning HOT 3
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