Full metadata
Title
Yuksel Splines for Probabilistic Sequence Prediction
Description
Current methods for sequence prediction often fail to account for higher-ordercontinuity. This results in the prediction of sequences that might be continuous but not
physically viable as I investigate higher-order smoothness in terms of velocity and
acceleration. Hence, I propose a Yuksel Spline-based model that is not only capable of
predicting curves that are guaranteed to be C^2 continuous but, also efficient to compute
as well. Characteristic properties of the models are demonstrated over toy examples and
sequence prediction tasks.
Date Created
2024
Contributors
- Tokas, Bhanu (Author)
- Kerner, Hannah (Thesis advisor)
- Lee, Kookjin (Committee member)
- Boscovic, Dragan (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
42 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.193596
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2024
Field of study: Computer Science
System Created
- 2024-05-02 02:16:16
System Modified
- 2024-05-02 02:16:22
- 6 months 1 week ago
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