Max Tegmark, Marin Soljačić, Fabian Ruehle, Ziming Liu, Yixuan Wang, Sachin Vaidya, James Halverson, Thomas Hou
We lifted 45 functions out of this paper's own repositories and ran 29 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| kindxiaoming/pykan | canonical | 1 of 2 |
| KindXiaoming/pykan | canonical | 0 of 4 |
| jloveric/high-order-layers-torch | pwc_unofficial | 8 of 10 |
| jaouadt/kanu_net | — | 6 of 7 |
| ivandrokin/torch-conv-kan | — | 2 of 2 |
| engichang1467/Simple-KAN | — | 2 of 2 |
| Ipsedo/KolmogorovArnoldNetworks | — | 1 of 4 |
| hoangthangta/all-kan | — | 1 of 2 |
| cg80499/kan-gpt-2 | — | 1 of 2 |
| chenziwenhaoshuai/Vision-KAN | — | 1 of 2 |
| athanasiosdelis/faster-kan | — | 1 of 2 |
| Blealtan/efficient-kan | — | 1 of 2 |
| synodicmonth/chebykan | — | 1 of 1 |
| spacelearner/jacobikan | — | 1 of 1 |
| 1ssb/torchkan | — | 1 of 1 |
| firasbdarwish/convkan3d | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| AF_KANLayer | Ran | hoangthangta/all-kan/models/af_kan.py code served (permissive licence) · get_code("07caa2645c6f88dc") |
| BSplineFunction | Ran | jaouadt/kanu_net/src/fastkanconv.py pointer only (licence: NONE) · get_code("b92aedeeb42c44a3") |
| ChebyKAN | Ran | cg80499/kan-gpt-2/transformer.py pointer only (licence: NONE) · get_code("b6a3b5c6796392f6") |
| ChebyKANLayer | Ran | synodicmonth/chebykan/ChebyKANLayer.py pointer only (licence: NONE) · get_code("d78f0ff6a36bad3a") |
| ChebyshevFunction | Ran | jaouadt/kanu_net/src/fastkanconv.py pointer only (licence: NONE) · get_code("df26e00e41461ac5") |
| FourierBasisFunction | Ran | jaouadt/kanu_net/src/fastkanconv.py pointer only (licence: NONE) · get_code("67e96346db6b0aeb") |
| JacobiKANLayer | Ran | spacelearner/jacobikan/JacobiKANLayer.py pointer only (licence: NONE) · get_code("1710ab92a3464cb1") |
| KAN | Ran | 1ssb/torchkan/torchkan.py code served (permissive licence) · get_code("0baa1b88f9939a21") |
| KAN | Ran | ivandrokin/torch-conv-kan/kans/kan.py code served (permissive licence) · get_code("02d69923ac1319f1") |
| KAN | Ran | engichang1467/Simple-KAN/model.py pointer only (licence: NONE) · get_code("ca2c6d9bc4073b91") |
| KANLayer | Ran | ivandrokin/torch-conv-kan/kans/kan.py code served (permissive licence) · get_code("166660fec1a43c38") |
| KANLinear | Ran | firasbdarwish/convkan3d/src/ConvKAN3D/efficient_kan.py pointer only (licence: GPL-3.0) · get_code("1c4b9e66c3895df6") |
| KANLinear | Ran | engichang1467/Simple-KAN/model.py pointer only (licence: NONE) · get_code("ee552720e93579c6") |
| KANLinear | Ran | chenziwenhaoshuai/Vision-KAN/ekan.py code served (permissive licence) · get_code("bba5f2e58a12a52a") |
| KANLinear | Ran | athanasiosdelis/faster-kan/efficient_kan/kan.py code served (permissive licence) · get_code("cd044fcb8e3f44fc") |
| KANLinear | Ran | Blealtan/efficient-kan/src/efficient_kan/kan.py code served (permissive licence) · get_code("28166672e87da391") |
| LinearKAN | Ran | Ipsedo/KolmogorovArnoldNetworks/kan/networks/networks.py pointer only (licence: NONE) · get_code("c159d03d56ef3105") |
| PolynomialFunction | Ran | jaouadt/kanu_net/src/fastkanconv.py pointer only (licence: NONE) · get_code("4d9332c12687c65a") |
| RadialBasisFunction | Ran | jaouadt/kanu_net/src/fastkanconv.py pointer only (licence: NONE) · get_code("432049a474dabd46") |
| SplineConv2D | Ran | jaouadt/kanu_net/src/fastkanconv.py pointer only (licence: NONE) · get_code("e31c4c404b98d796") |
| chebyshevLobatto | Ran | jloveric/high-order-layers-torch/high_order_layers_torch/LagrangePolynomial.py code served (permissive licence) · get_code("9c1513ccebbda096") |
| conv_transpose_wrapper | Ran | jloveric/high-order-layers-torch/high_order_layers_torch/FunctionalConvolutionTranspose.py code served (permissive licence) · get_code("5ce9da957bc84eec") |
| conv_wrapper | Ran | jloveric/high-order-layers-torch/high_order_layers_torch/FunctionalConvolution.py code served (permissive licence) · get_code("654325325ef70521") |
| get_lagrange_basis | Ran | jloveric/high-order-layers-torch/high_order_layers_torch/LagrangePolynomial.py code served (permissive licence) · get_code("c9ec66053c2c6c3d") |
| max_abs | Ran | jloveric/high-order-layers-torch/high_order_layers_torch/utils.py code served (permissive licence) · get_code("e6a067cf62cc8c8b") |
| max_abs_normalization | Ran | jloveric/high-order-layers-torch/high_order_layers_torch/utils.py code served (permissive licence) · get_code("a6d502baf050617f") |
| max_abs_normalization_last | Ran | jloveric/high-order-layers-torch/high_order_layers_torch/utils.py code served (permissive licence) · get_code("a15c9905e0c2add9") |
| scalar_to_list | Ran | jloveric/high-order-layers-torch/high_order_layers_torch/networks.py code served (permissive licence) · get_code("463cbaa8879bc79d") |
| sparse_mask | Ran | kindxiaoming/pykan/kan/KANLayer.py code served (permissive licence) · get_code("2f28fd290c4d9bd9") |
| AF_KAN | Not yet run | hoangthangta/all-kan/models/af_kan.py code served (permissive licence) · get_code("b00cb7f7deabbeab") |
| ActivationFunction | Not yet run | Ipsedo/KolmogorovArnoldNetworks/kan/networks/networks.py pointer only (licence: NONE) · get_code("36bad22a5b2a35bb") |
| FastKANConvLayer | Not yet run | jaouadt/kanu_net/src/fastkanconv.py pointer only (licence: NONE) · get_code("5be468792b0cf15a") |
| InfoModule | Not yet run | Ipsedo/KolmogorovArnoldNetworks/kan/networks/networks.py pointer only (licence: NONE) · get_code("b8d00a4f1cb48922") |
| KAN | Not yet run | chenziwenhaoshuai/Vision-KAN/ekan.py code served (permissive licence) · get_code("9775b6b471f0efe7") |
| KAN | Not yet run | athanasiosdelis/faster-kan/efficient_kan/kan.py code served (permissive licence) · get_code("0406a19e6e415352") |
| KAN | Not yet run | Blealtan/efficient-kan/src/efficient_kan/kan.py code served (permissive licence) · get_code("32c87d6531990ab4") |
| KANLayer | Not yet run | kindxiaoming/pykan/kan/KANLayer.py code served (permissive licence) · get_code("81f6b6bcc71a3151") |
| KANLayer | Not yet run | cg80499/kan-gpt-2/transformer.py pointer only (licence: NONE) · get_code("6c165e56ec04e1b9") |
| LinearKanLayers | Not yet run | Ipsedo/KolmogorovArnoldNetworks/kan/networks/networks.py pointer only (licence: NONE) · get_code("2c906ad0c6bcc8fb") |
| fixed_rotation_layer | Not yet run | jloveric/high-order-layers-torch/high_order_layers_torch/layers.py code served (permissive licence) · get_code("6467947a4d7fb147") |
| get_feynman_dataset | Not yet run | KindXiaoming/pykan/kan/feynman.py code served (permissive licence) · get_code("66c87ccdfecd9980") |
| next_nontrivial_operation | Not yet run | KindXiaoming/pykan/kan/compiler.py code served (permissive licence) · get_code("5a7aee504b816c90") |
| pareto_frontier | Not yet run | KindXiaoming/pykan/kan/experiment.py code served (permissive licence) · get_code("5a09c168cd818718") |
| runner1 | Not yet run | KindXiaoming/pykan/kan/experiment.py code served (permissive licence) · get_code("2d265a6adde38491") |
| select_network | Not yet run | jloveric/high-order-layers-torch/high_order_layers_torch/modules.py code served (permissive licence) · get_code("1e0e36dc3aa7b0f3") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable activation functions on edges ("weights"). KANs have no linear weights at all -every weight parameter is replaced by a univariate function parametrized as a spline. We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability, on small-scale AI + Science tasks. For accuracy, smaller KANs can achieve comparable or better accuracy than larger MLPs in function fitting tasks. Theoretically and empirically, KANs possess faster neural scaling laws than MLPs. For interpretability, KANs can be intuitively visualized and can easily interact with human users. Through two examples in mathematics and physics, KANs are shown to be useful "collaborators" helping scientists (re)discover mathematical and physical laws. In summary, KANs are promising alternatives for MLPs, opening opportunities for further improving today's deep learning models which rely heavily on MLPs.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2404.19756")
get_code_for_paper("2404.19756")
have("2404.19756")
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