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Modular Neural Networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modular Neural Networks./
作者:	Sakryukin, Andrey.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	139 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-07, Section: B.
Contained By:	Dissertations Abstracts International83-07B.
標題:	Signal transduction. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28832594
ISBN:	9798460441884

Modular Neural Networks.
Sakryukin, Andrey.

Modular Neural Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 139 p.

Source: Dissertations Abstracts International, Volume: 83-07, Section: B.

Thesis (Ph.D.)--National University of Singapore (Singapore), 2021.

This item must not be sold to any third party vendors.

The term Modular Network was known before its application to Neural Networks and was defined multiple times in different areas of research, including Applied Mathematics, Social Sciences, Biology, and others. Li and Shuurmans defined modularity as a function quantifying the quality of a network division into communities. So Modular Networks (MN) - are the networks containing highly connected regions (communities), which are sparsely connected to the rest of the network. In this work we study the evolvement of different types of Modular Networks in the field of deep learning. The focus of our research is in the advantages of such architectures applied to different domains and tasks. We start by introducing the advantages of modular architectures in addressing high-dimensional output space problems, as well as proposing a novel uncertainty-based strategy for the structure inference. Next, we study the application of MNs to the problem of incremental learning and functionality disentanglement. Finally, we discuss advantages of modular architectures in sparse-reward problems of reinforcement learning.

ISBN: 9798460441884Subjects--Topical Terms:

3546420
Signal transduction.
Subjects--Index Terms:

Modular Network

Modular Neural Networks.
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506 $a This item must not be sold to any third party vendors.
506 $a This item must not be sold to any third party vendors.
520 $a The term Modular Network was known before its application to Neural Networks and was defined multiple times in different areas of research, including Applied Mathematics, Social Sciences, Biology, and others. Li and Shuurmans defined modularity as a function quantifying the quality of a network division into communities. So Modular Networks (MN) - are the networks containing highly connected regions (communities), which are sparsely connected to the rest of the network. In this work we study the evolvement of different types of Modular Networks in the field of deep learning. The focus of our research is in the advantages of such architectures applied to different domains and tasks. We start by introducing the advantages of modular architectures in addressing high-dimensional output space problems, as well as proposing a novel uncertainty-based strategy for the structure inference. Next, we study the application of MNs to the problem of incremental learning and functionality disentanglement. Finally, we discuss advantages of modular architectures in sparse-reward problems of reinforcement learning.
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