Comments (6)
Hi Jannik,
I think here fc_o
(in the 5th line of the code you pasted) is the W_O
in the paper, what do you think?
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Dear Jingweiz,
thanks for your reply!
I would have identified fc_o
with the rFF(H)
of the MAB and not with W_O
.
from set_transformer.
Hi, thanks for your interest!
- Multiplying W_0 after the concat and 2) multiplying W to the query to get Q and then split-attend-concat, in essence, makes a small difference (one of them is a restricted version of another). For the paper, I followed the description in the original transformer paper, and for the code, I chose the current form following the code available for original transfomer (also, it gives a cleaner code). But they don't make a big empirical difference.
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Hey Juho Lee!
Thanks for your reply.
It makes sense that this does not give a big empirical difference. I just wanted to check if I missed something.
And LayerNorm(X + Multihead(X, Y, Y ; ω))
in the paper, should probably be something like LayerNorm(W_q X + Multihead(X, Y, Y ; ω))
, correct?
from set_transformer.
Dear Jingweiz,
thanks for your reply!
I would have identifiedfc_o
with therFF(H)
of the MAB and not withW_O
.
Oh right exactly, I got messed up, thanks!
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I have a follow up question linked to this topic.
In the paper a row-wise FF block is used for the pooling, and unlike the rFF in the MAB, the rFF in the pooling doesn't have an activation function. Should the PMA rFF have an activation function or not?
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Related Issues (19)
- License HOT 1
- about shape classification experiment HOT 1
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- LayerNorm HOT 1
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