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traditional neural networksIEEE Spectrum
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81% Informative
New Kolmogorov-Arnold Networks are more interpretable and more accurate, proponents say.
Developers say the way they learn to represent physics data concisely could help scientists uncover new laws of nature.
In traditional neural networks, each synapse learns a number called a weight, and each neuron applies a simple function to the sum of its inputs.
Dozens of papers have already cited the KAN preprint.
KANs could help physicists discover high-temperature superconductors or ways to control nuclear fusion.
One downside is that they take longer per parameter to train, but they need fewer parameters.
Liu says training time won't be an issue at the smaller scale of many physics problems.
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