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Neural Tangent Kernel (NTK) was invented by Rohit Raghavan and Vijay Pudipeddi, a research team from the University of Utah. The Neural Tangent Kernel is a computationally efficient variant of the standard NTK kernel, allowing for faster estimation of kernel matrices, enabling faster computation of kernel matrix inverse estimates, faster computation of KL divergence between data and NTK kernel, faster optimization of regularization hyperparameters and faster exploration of regularization trade-offs in the NTK learning problem
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