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Empirical Evaluations of Transformers.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Empirical Evaluations of Transformers./
Author:	McGuire, Jack.
Description:	1 online resource (139 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-05.
Contained By:	Masters Abstracts International85-05.
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798380846868

Empirical Evaluations of Transformers.
McGuire, Jack.

Empirical Evaluations of Transformers. - 1 online resource (139 pages)

Source: Masters Abstracts International, Volume: 85-05.

Thesis (M.S.)--Rutgers The State University of New Jersey, School of Graduate Studies, 2023.

Includes bibliographical references

The transformer architecture of neural networks immediately took over the machine learning world upon the release of the paper "Attention is All You Need" by a team at Google Brain in 2017. But, perhaps more importantly, the Summer and Fall 2022 releases of generative networks such as ChatGPT, Midjourney, and DALL-E 2 saw the technology make an immediate impact on millions of people unrelated to the academic circles that had been talking about transformers for several years, shaking up creative industries and introducing the technology to many people with widely varying understandings of how it works.Thus, we set out to evaluate temporal sequence models such as transformers; first, on a deeply technical level, investigating the role of one of transformers' unique contributions, positional encoding, on the overall training time and efficacy of the system. We then evaluated the system empirically in a broader sense as a tool that a common person would use, in order to quantitatively identify shortcomings and misconceptions on what the systems are and are not able to accomplish. Finally, we looked at another more established temporal sequence model in the form of reinforcement learning in order to understand hierarchical representation of such models and discuss the utility of hierarchical models of transformers in the future.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380846868Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

ChatGPTIndex Terms--Genre/Form:

554714
Electronic books.

Empirical Evaluations of Transformers.
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504 $a Includes bibliographical references
520 $a The transformer architecture of neural networks immediately took over the machine learning world upon the release of the paper "Attention is All You Need" by a team at Google Brain in 2017. But, perhaps more importantly, the Summer and Fall 2022 releases of generative networks such as ChatGPT, Midjourney, and DALL-E 2 saw the technology make an immediate impact on millions of people unrelated to the academic circles that had been talking about transformers for several years, shaking up creative industries and introducing the technology to many people with widely varying understandings of how it works.Thus, we set out to evaluate temporal sequence models such as transformers; first, on a deeply technical level, investigating the role of one of transformers' unique contributions, positional encoding, on the overall training time and efficacy of the system. We then evaluated the system empirically in a broader sense as a tool that a common person would use, in order to quantitatively identify shortcomings and misconceptions on what the systems are and are not able to accomplish. Finally, we looked at another more established temporal sequence model in the form of reinforcement learning in order to understand hierarchical representation of such models and discuss the utility of hierarchical models of transformers in the future.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
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650 4 $a Computer engineering. $3 569006
653 $a ChatGPT
653 $a Large language models
653 $a Machine learning
653 $a Positional encoding
653 $a Transformers
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