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What Aidan N. Gomez thinks about transformers

@aidan-gomez · 3 positions · 0 changes of mind

One of the eight authors of the 2017 transformer paper "Attention Is All You Need", then at University of Toronto; per the paper's own contribution note, designed and implemented parts of tensor2tensor, the codebase that replaced the team's earlier one.

Aidan N. Gomez did not write this page.

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3 dated positions, 2017, in their own words. Our reading of what Aidan N. Gomez has said — not written or endorsed by them.

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  1. Our readingSequence transduction can use a simple architecture based solely on attention, without recurrence or convolutions.

    Their wordsWe propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.

    ↗Attention is all you needarxiv.org 6th of 24 in this piece

  3. Our readingA transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution.

    Their wordsTo the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.

    ↗Attention is all you needarxiv.org 14th of 24 in this piece

  5. Our readingSelf-attention can yield more interpretable models.

    Their wordsAs side benefit, self-attention could yield more interpretable models.

    ↗Attention is all you needarxiv.org 22nd of 24 in this piece