國立虎尾科技大學 |

Concept-Monitor : = Using Concept Embeddings to Understand Neural Net Training.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Concept-Monitor :/
其他題名:	Using Concept Embeddings to Understand Neural Net Training.
作者:	Khan, Mohammad Ali.
面頁冊數:	1 online resource (51 pages)
附註:	Source: Masters Abstracts International, Volume: 84-12.
Contained By:	Masters Abstracts International84-12.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9798379761134

Concept-Monitor : = Using Concept Embeddings to Understand Neural Net Training.
Khan, Mohammad Ali.

Concept-Monitor :Using Concept Embeddings to Understand Neural Net Training. - 1 online resource (51 pages)

Source: Masters Abstracts International, Volume: 84-12.

Thesis (M.S.)--University of California, San Diego, 2023.

Includes bibliographical references

In this work, we propose a general framework called Concept-Monitor to help demystify the black-box DNN training processes automatically using a novel unified embedding space and concept diversity metric. Concept-Monitor enables human-interpretable visualization and indicators of the DNN training processes and facilitates transparency as well as deeper understanding on how DNNs develop along the during training. Inspired by these findings, we also propose a new training regularizer that incentivizes hidden neurons to learn diverse concepts, which we show to improve training performance. Finally, we apply Concept-Monitor to conduct several case studies on different training paradigms including adversarial training, fine-tuning and network pruning via the Lottery Ticket Hypothesis.

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

Mode of access: World Wide Web

ISBN: 9798379761134Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Explainable AIIndex Terms--Genre/Form:

554714
Electronic books.

Concept-Monitor : = Using Concept Embeddings to Understand Neural Net Training.
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504 $a Includes bibliographical references
520 $a In this work, we propose a general framework called Concept-Monitor to help demystify the black-box DNN training processes automatically using a novel unified embedding space and concept diversity metric. Concept-Monitor enables human-interpretable visualization and indicators of the DNN training processes and facilitates transparency as well as deeper understanding on how DNNs develop along the during training. Inspired by these findings, we also propose a new training regularizer that incentivizes hidden neurons to learn diverse concepts, which we show to improve training performance. Finally, we apply Concept-Monitor to conduct several case studies on different training paradigms including adversarial training, fine-tuning and network pruning via the Lottery Ticket Hypothesis.
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653 $a DNN training processes
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