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Comment on Every Model Learned by Gradient Descent Is Approximately a Kernel Machine

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Is this a new finding? I'm not an expert on the deeper mathematical side of ML, but I remember a friend of mine already telling me about something that sounded exactly like this (ca. 2016-17).

The fact that kernel machines can approximate other models isn't new. I think the novel idea is that they have explicitly constructed the "translation" between the arbitrary model to the kernel machine, and it's quite clean.

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