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In 2017, the original (100M-sized) encoder-decoder transformer model was proposed in the "Attention is all you need" paper. At the time, the focus of the research was on improving seq2seq for machine translation , by removing its recurrence to process all tokens in parallel, but preserving its dot-product attention mechanism to keep its text ...
In 2017, the original (100M-sized) encoder-decoder transformer model was proposed in the "Attention is all you need" paper. At the time, the focus of the research was on improving seq2seq for machine translation , by removing its recurrence to process all tokens in parallel, but preserving its dot-product attention mechanism to keep its text ...
For decoder self-attention, all-to-all attention is inappropriate, because during the autoregressive decoding process, the decoder cannot attend to future outputs that has yet to be decoded. This can be solved by forcing the attention weights w i j = 0 {\displaystyle w_{ij}=0} for all i < j {\displaystyle i<j} , called "causal masking".
Vaswani's most notable work is the paper "Attention Is All You Need", published in 2017. [7]The paper introduced the Transformer model, which eschews the use of recurrence in sequence-to-sequence tasks and relies entirely on self-attention mechanisms.
Encoder: a stack of Transformer blocks with self-attention, but without causal masking. Task head: This module converts the final representation vectors into one-hot encoded tokens again by producing a predicted probability distribution over the token types.
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A 2019 paper [8] applied ideas from the Transformer to computer vision. Specifically, they started with a ResNet, a standard convolutional neural network used for computer vision, and replaced all convolutional kernels by the self-attention mechanism found in a Transformer. It resulted in superior performance.
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