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Empowering Deep Learning with Graphs.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Empowering Deep Learning with Graphs./
作者:	You, Jiaxuan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	206 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-07, Section: B.
Contained By:	Dissertations Abstracts International83-07B.
標題:	Deep learning. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28847527
ISBN:	9798762116251

Empowering Deep Learning with Graphs.
You, Jiaxuan.

Empowering Deep Learning with Graphs. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 206 p.

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

Thesis (Ph.D.)--Stanford University, 2021.

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

Deep learning has reshaped the research and applications in artificial intelligence. Modern deep learning models are primarily designed for regular-structured data, such as sequences and images. These models are built for tasks that take these regular-structured data as the input (e.g., classification, regression), as the output (e.g., generation), or as the structural prior (e.g., neural architecture design). However, not all forms of data are regular-structured. One notable example is graph-structured data, a general and powerful data structure that represents entities and their relationships in a succinct form. While graph-structured data is ubiquitous throughout the natural and social sciences, its discrete and non-i.i.d. nature brings unique challenges to modern deep learning models.In this thesis, we aim to empower deep learning with graph-structured data, by facilitating deep learning models to take graphs as the input, the output, and the prior. My research in these three directions has opened new frontiers for deep learning research: (1) Learning from graphs with deep learning. We develop expressive and effective deep learning methods that can take graphs as the input, which promotes the learning and understanding of graphs. (2) Generation of graphs with deep learning. We formulate the generation process of graphs using deep learning models, which advances the discovery and design of graphs. (3) Graph as the prior for deep learning. We discover that graph structure can serve as a powerful prior for neural architectures and machine learning tasks, which opens a new direction for the design and understanding of deep learning. Finally, we discuss the wide applications of the above-mentioned techniques, including recommender systems, drug discovery, neural architecture design, and missing data imputation.

ISBN: 9798762116251Subjects--Topical Terms:

3554982
Deep learning.

Empowering Deep Learning with Graphs.
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