Recorded2017-12-04 to 2017-12-09rangescience

The transformer is presented at NeurIPS

Vaswani and seven co-authors presented "Attention Is All You Need" at the 31st Conference on Neural Information Processing Systems in Long Beach, proposing a sequence architecture built on attention alone, without recurrence or convolution, and demonstrating it on two machine translation tasks with better quality and substantially less training time. The removal of sequential dependency was the consequential part: it made training throughput a function of how many accelerators could be pointed at the problem, and thereby made very large training runs an engineering question rather than a scheduling impossibility.

Historical — it happened, and the record is settled.

Subjects

The transformer architecture · technology · not located
Machine translation · technology · not located

Evidence

Partly verified — Core facts are sourced; some optional detail is deliberately absent.

Attention Is All You Need
The transformer replaces recurrence and convolution with attention alone, is more parallelisable, and was shown superior in quality on two machine translation tasks while requiring significantly less time to train. V55 verification basis: the proceedings were not fetched; search results carried the author list, the venue, the Long Beach dates of 4–9 December 2017, the page range and the substance of the abstract and attributed them to the NeurIPS proceedings. The paper's arXiv identifier, its 2017 preprint date and its reported BLEU scores were explicitly NOT confirmed by retrieval and are not asserted anywhere in this pack.
Advances in Neural Information Processing Systems 30 (NeurIPS 2017) · 2017-12 · Partly verified
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł. and Polosukhin, I., Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, 4–9 December 2017, pp. 6000–6010

Recorded on The transformer architecture

2017-12-04 to 2017-12-09The transformer is presented at NeurIPS
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Related because they share a subject in the Atlas — never because the text looks similar.

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