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Mean field theory treats very wide randomly initialized neural networks as averaging machines where each neuron behaves like a sample from a common distribution.
Neural Tangent Kernel (NTK) describes how wide neural networks train like kernel machines, turning gradient descent into kernel regression in the infinite-width limit.