2026-09-18T20:29:48Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1245722026-06-06T00:49:38Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Duane S. Boning.
Grullon, Dylan Emanuel Centeno.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2020-04-13T18:21:52Z
2020-04-13T18:21:52Z
2019
2019
https://hdl.handle.net/1721.1/124572
1149026032
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 85-86).
In this thesis, we design and explore a new model architecture called a Variational Bayes Recurrent Neural Network (VBRNN) for modelling time series. The VBRNN contains explicit structure to disentangle time constant and time dependent dynamics for use with compatible time series, such as those that can be modelled by differential equations with time constant parameters and time dependent state. The model consists of a Variational Bayes (VB) layer to infer time constant state, as well as a conditioned-RNN to model time dependent dynamics. The VBRNN is explored through various synthetic datasets and problems, and compared to conventional methods on these datasets. This approach demonstrates effective disentanglement, motivating future work to explore the efficacy of this mo del in real word datasets.
by Dylan Emanuel Centeno Grullon.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
86 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Disentangling time constant and time dependent hidden state in time series with variational Bayesian inference
Thesis
application/pdf
2020-04-13T18:21:23Z
Publication
Master
EECS
unknown
unknown
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<Abstract>In this thesis, we design and explore a new model architecture called a Variational Bayes Recurrent Neural Network (VBRNN) for modelling time series. The VBRNN contains explicit structure to disentangle time constant and time dependent dynamics for use with compatible time series, such as those that can be modelled by differential equations with time constant parameters and time dependent state. The model consists of a Variational Bayes (VB) layer to infer time constant state, as well as a conditioned-RNN to model time dependent dynamics. The VBRNN is explored through various synthetic datasets and problems, and compared to conventional methods on these datasets. This approach demonstrates effective disentanglement, motivating future work to explore the efficacy of this mo del in real word datasets.</Abstract>
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