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Advancing Sequence Modeling With Dee...

Smith, James Thomas Howard,

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Advancing Sequence Modeling With Deep State Space Methods /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advancing Sequence Modeling With Deep State Space Methods // James Thomas Howard Smith.
Author:	Smith, James Thomas Howard,
Description:	1 electronic resource (216 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-07, Section: B.
Contained By:	Dissertations Abstracts International86-07B.
Subject:	Deep learning. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31821810
ISBN:	9798302377395

Advancing Sequence Modeling With Deep State Space Methods /
Smith, James Thomas Howard,

Advancing Sequence Modeling With Deep State Space Methods /James Thomas Howard Smith. - 1 electronic resource (216 pages)

Source: Dissertations Abstracts International, Volume: 86-07, Section: B.

This thesis advances sequence modeling through novel contributions bridging state space models and deep learning. Sequence modeling techniques identify patterns and relationships in ordered data, enabling diverse applications including predicting time series, processing speech and audio signals, generating text, modeling biological sequences, and analyzing neural recordings. While sequence modeling has been transformed by deep learning, current architectures face fundamental tradeoffs between computational efficiency, modeling capability, and interpretability. We address these challenges through three complementary contributions.We first introduce the S5 layer, which redesigns deep state space methods to enable parallel scan operations. We establish mathematical connections with prior methods that explain its strong empirical performance. We then extend these insights to spatiotemporal modeling with ConvS5, which integrates spatial structure into the state space framework. Finally, we develop new analytical tools through the Jacobian Switching Linear Dynamical System (JSLDS) procedure, providing a systematic way to decompose complex nonlinear dynamics into interpretable linear components.Through extensive empirical validation across a variety of domains, we show how these methods significantly advance the state-of-the-art in their respective areas. Together, these contributions demonstrate the broad potential of combining classical dynamical systems approaches with modern deep learning techniques to create more capable, efficient, and interpretable sequence models.

English

ISBN: 9798302377395Subjects--Topical Terms:

3554982
Deep learning.

Advancing Sequence Modeling With Deep State Space Methods /
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520 $a This thesis advances sequence modeling through novel contributions bridging state space models and deep learning. Sequence modeling techniques identify patterns and relationships in ordered data, enabling diverse applications including predicting time series, processing speech and audio signals, generating text, modeling biological sequences, and analyzing neural recordings. While sequence modeling has been transformed by deep learning, current architectures face fundamental tradeoffs between computational efficiency, modeling capability, and interpretability. We address these challenges through three complementary contributions.We first introduce the S5 layer, which redesigns deep state space methods to enable parallel scan operations. We establish mathematical connections with prior methods that explain its strong empirical performance. We then extend these insights to spatiotemporal modeling with ConvS5, which integrates spatial structure into the state space framework. Finally, we develop new analytical tools through the Jacobian Switching Linear Dynamical System (JSLDS) procedure, providing a systematic way to decompose complex nonlinear dynamics into interpretable linear components.Through extensive empirical validation across a variety of domains, we show how these methods significantly advance the state-of-the-art in their respective areas. Together, these contributions demonstrate the broad potential of combining classical dynamical systems approaches with modern deep learning techniques to create more capable, efficient, and interpretable sequence models.
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