Attention layers--which map a sequence of inputs to a sequence of outputs--are core building blocks of the Transformer architecture which has achieved significant

Neural Information Processing Systems 

We show that such a random-feature attention layer can express a broad class of target functions that are permutation invariant to the key vectors.

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