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This work introduces feature instability (FI), the feature-axis analogue of instance instability (II), which measures sensitivity to removing a single feature, and shows that FI captures generalization-relevant information complementary to II.
This work investigates the expressiveness of general query/key rotations and finds that any normalized continuous positive-definite attention modulation function can be approximated by random rotations induced by its own Fourier transform, which is term Random Fourier Rotations.
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