자료유형  

 학위논문

Control Number  

0017161482

International Standard Book Number  

9798382232119

Dewey Decimal Classification Number  

621.3

Main Entry-Personal Name  

Karan Goel.

Publication, Distribution, etc. (Imprint  

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

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

153 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

General Note  

Advisor: Re, Christopher.

Dissertation Note  

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

Summary, Etc.  

요약Sequence modeling primitives have been responsible for breakthroughs across domains like natural language processing and genomics. Despite these advances, existing primitives still struggle to model the large class of signal data acquired from physical sensors. This data has unique characteristics that make it challenging to model: signal data resolution affects the training and generalization of models, signal data is sampled at high rates, resulting in dense data with long-range dependencies, and signal data is highly diverse, with application areas including healthcare, video processing, and industrial sensing. All of these properties raise the bar for universal approaches to modeling this data. This thesis develops a new set of approaches for modeling signal data using state space models. First, we introduce a sequence model called S4 that serves as a general building block for modeling signal data. Second, we generalize this modeling layer to multidimensional signals like images and videos, yielding the first state-of-the-art signal model on large-scale benchmarks such as ImageNet. Incorporating S4 into a multiscale architecture makes it possible to model extremely long sequences of audio, including on a previously unsolved task involving unconditional autoregressive generation of raw audio samples. Finally, we demonstrate the widespread applicability of our approach to a variety of signal data, including a real-world application involving impedance sensor data used in the diagnosis of gastroesophageal reflux disease. Taken together, this new set of approaches provides a universal and versatile set of primitives for modeling diverse, multidimensional signals.

Subject Added Entry-Topical Term  

Computer engineering.

Subject Added Entry-Topical Term  

Information technology.

Index Term-Uncontrolled  

Sequence modeling primitives

Index Term-Uncontrolled  

Natural language processing

Index Term-Uncontrolled  

Multidimensional signals

Added Entry-Corporate Name  

Stanford University.

Host Item Entry  

Dissertations Abstracts International. 85-11B.

Electronic Location and Access  

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Control Number  

joongbu:658238