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[D] Computing `q dot q` instead of `q dot k` when calculating scores for self-attention in Transformer

Going through the Transformer paper, and its implementation, I have had a question:

In the self-attention routine in the encoder, is it plausible to compute q dot q instead of q dot k when calculating scores for each input token?

I see that in the self-attention, the memory_antecedent = query_antecedent and q, k, v is computed (and trained) separately (c.f. compute_qkv in T2T).

Would utilizing the same q for the computation of scores (rather than having a separate k) seriously deteriorate the performance?

submitted by /u/kingsiguk
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.