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When implementing cosformer on MultiHeadAttention in Transformer-XL and running without extra long-range memory, the ReLU performance is worse than eLU. I think it is because the Attention and FF Net are different since XL-like transformer has different layer norm and residual connection. Why this ReLU(Q)ReLU(K).T softmax replacement is not robust on different transformer architectures?
The text was updated successfully, but these errors were encountered:
When implementing cosformer on MultiHeadAttention in Transformer-XL and running without extra long-range memory, the ReLU performance is worse than eLU. I think it is because the Attention and FF Net are different since XL-like transformer has different layer norm and residual connection. Why this ReLU(Q)ReLU(K).T softmax replacement is not robust on different transformer architectures?
The text was updated successfully, but these errors were encountered: