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No, the kernel is a mixing of gradients from the training. The proof depends completely on the particular form of optimization, in this case gradient descent in
by l33tman 6y ago
No, the kernel is a mixing of gradients from the training. The proof depends completely on the particular form of optimization, in this case gradient descent in the limit of infinitesimal "timestepping", and suggests that gradient descent optimization doesn't cleverly pick good internal representations of the data.
- leereeves 6y ago> suggests that gradient descent optimization doesn't cleverly pick good internal representations of the data. How so? The existence of equivalent kernels doesn't imply they aren't clever or good.