자료유형  

 학위논문

Control Number  

0016933399

International Standard Book Number  

9798379725426

Dewey Decimal Classification Number  

004

Main Entry-Personal Name  

Pooladzandi, Omead Brandon.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of California, Los Angeles., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(189 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

General Note  

Advisor: Pottie, Gregory J.

Dissertation Note  

Thesis (Ph.D.)--University of California, Los Angeles, 2023.

Restrictions on Access Note  

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

Summary, Etc.  

요약Effective natural agents excel in learning representations of our world and efficiently generalizing to make decisions. Critically, developing such advanced reasoning capabilities can occur even with limited information-rich samples. In stark contrast, the major success of deep learning-based artificial agents is primarily trained on massive datasets. This dissertation focuses on curvature-informed learning and generative modeling methods that boost efficiency and close the gap between natural and artificial agents, thus enabling computationally efficient and improved reasoning.This dissertation is comprised of two parts. First, we formally lay the foundations for learning. The goal is to establish optimization techniques, understand datasets, establish probabilistic generative models, and provide natural learning objectives even in settings with limited supervision. We discuss various first and second-order optimization methods, show the importance of modeling distributions in Variational Auto Encoders (VAEs),and discuss which points are essential for generalization in supervised learning. Building on these insights, we develop new algorithms to boost the performance of state-of-the-art models, select subsets to improve data quality, speed up training, mitigate their biases, and generate new augmentations on large labeled and partially labeled datasets. These contributions enable ML systems to better model and generalize to unseen and potentially out-of-distribution samples while drastically reducing training time and computational cost.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Computer engineering.

Index Term-Uncontrolled  

Computer vision

Index Term-Uncontrolled  

Curvature aware optimization

Index Term-Uncontrolled  

Generative models

Index Term-Uncontrolled  

Machine learning

Index Term-Uncontrolled  

Speech processing

Added Entry-Corporate Name  

University of California, Los Angeles Electrical and Computer Engineering 0333

Host Item Entry  

Dissertations Abstracts International. 84-12B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:640139