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The kernel (lazy) regime keeps neural network parameters close to their initialization, making training equivalent to kernel regression with a fixed kernel such as the Neural Tangent Kernel (NTK).
The Neural Tangent Kernel (NTK) connects very wide neural networks to classical kernel methods, letting us study training as if it were kernel regression.