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Computer Science > Machine Learning

arXiv:2207.09238 (cs)
[Submitted on 19 Jul 2022]

Title:Formal Algorithms for Transformers

Authors:Mary Phuong, Marcus Hutter
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    Abstract: This document aims to be a self-contained,
    mathematically precise overview of transformer architectures and
    algorithms (*not* results). It covers what transformers are, how
    they are trained, what they are used for, their key architectural
    components, and a preview of the most prominent models. The
    reader is assumed to be familiar with basic ML terminology and
    simpler neural network architectures such as MLPs.

Comments: 16 pages, 15 algorithms
          Machine Learning (cs.LG); Artificial Intelligence (cs.AI);
Subjects: Computation and Language (cs.CL); Neural and Evolutionary
          Computing (cs.NE)
Cite as:  arXiv:2207.09238 [cs.LG]
          (or arXiv:2207.09238v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2207.09238
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Marcus Hutter [view email]
[v1] Tue, 19 Jul 2022 12:49:02 UTC (43 KB)
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