자료유형  

 학위논문

Control Number  

0017162972

International Standard Book Number  

9798384342045

Dewey Decimal Classification Number  

658

Main Entry-Personal Name  

Dax, Victoria Magdalena.

Publication, Distribution, etc. (Imprint  

[S.l.] : Stanford University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

116 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-03, Section: A.

General Note  

Advisor: Kochenderfer, Mykel.

Dissertation Note  

Thesis (Ph.D.)--Stanford University, 2024.

Summary, Etc.  

요약Graph Neural Networks (GNNs) have become important in the machine learning landscape because of their ability to model complex, structured data. This thesis presents approaches for blending GNNs with other deep learning methods, such as the decision-making capabilities of reinforcement learning (RL) or the generative abilities of variational auto-encoders (VAEs), to enhance the practical functionality of GNNs and to expand their applicability in various domains.The fundamental challenge addressed in this thesis is overcoming the inherent difficulties in integrating GNNs with other methodologies. GNNs excel in processing structured data but face issues like oversmoothing, particularly with increasing network depth. On the other hand, methods such as VAEs offer flexible generative abilities but have their own set of training and scalability challenges. And, while recurrent neural networks (RNNs) excel at processing temporal patterns, they introduce concerns of catastrophic forgetting and vanishing gradients. The solutions explored here involve novel combinations of these diverse techniques, aiming to leverage their strengths while mitigating their weaknesses.We start by exploring GNNs' theoretical properties, especially their generalization properties, before transitioning into practical applications. First, we demonstrate an enhancement of GNNs by integrating RNNs for advanced time-series predictions in interconnected systems. Then, we combine GNNs with variational auto-encoders (VAEs) to improve out-of-distribution generalizability and model interpretability through disentanglement of the embedding space in motion prediction tasks. We end by discussing using GNNs in deep RL techniques, specifically for combinatorial optimization tasks.

Subject Added Entry-Topical Term  

Decision making.

Subject Added Entry-Topical Term  

Neural networks.

Subject Added Entry-Topical Term  

Web studies.

Added Entry-Corporate Name  

Stanford University.

Host Item Entry  

Dissertations Abstracts International. 86-03A.

Electronic Location and Access  

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Control Number  

joongbu:657992