KANs

Kolmogorov-Arnold representation theorem Kolmogorov-Arnold representation theorem states that if is a multivariate continuous function on a bounded domain, then it can be written as a finite composition of continuous functions of a single variable and the binary operation of addition

Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
figures		figures
python=3.9.7		python=3.9.7
ve		ve
.gitignore		.gitignore
API_10_device.ipynb		API_10_device.ipynb
API_1_indexing.ipynb		API_1_indexing.ipynb
API_2_plotting.ipynb		API_2_plotting.ipynb
API_3_grid.ipynb		API_3_grid.ipynb
API_4_extract_activations.ipynb		API_4_extract_activations.ipynb
API_5_initialization_hyperparameter.ipynb		API_5_initialization_hyperparameter.ipynb
API_6_training_hyperparameter.ipynb		API_6_training_hyperparameter.ipynb
API_7_pruning.ipynb		API_7_pruning.ipynb
API_8_checkpoint.ipynb		API_8_checkpoint.ipynb
API_9_video.ipynb		API_9_video.ipynb
Example_10_relativity-addition.ipynb		Example_10_relativity-addition.ipynb
Example_11_encouraing_linear.ipynb		Example_11_encouraing_linear.ipynb
Example_12_unsupervised_learning.ipynb		Example_12_unsupervised_learning.ipynb
Example_13_phase_transition.ipynb		Example_13_phase_transition.ipynb
Example_1_function_fitting.ipynb		Example_1_function_fitting.ipynb
Example_2_deep_formula.ipynb		Example_2_deep_formula.ipynb
Example_3_classfication.ipynb		Example_3_classfication.ipynb
Example_4_symbolic_regression.ipynb		Example_4_symbolic_regression.ipynb
Example_5_special_functions.ipynb		Example_5_special_functions.ipynb
Example_6_PDE.ipynb		Example_6_PDE.ipynb
Example_7_continual_learning.ipynb		Example_7_continual_learning.ipynb
Example_8_scaling.ipynb		Example_8_scaling.ipynb
Example_9_singularity.ipynb		Example_9_singularity.ipynb
README.md		README.md
Start.ipynb		Start.ipynb
image.png		image.png
req.txt		req.txt

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