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A Reaction Network Scheme Which Implements Inference and Learning for Hidden Markov Models. / Singh, Abhinav; Wiuf, Carsten; Behera, Abhishek; Gopalkrishnan, Manoj.
DNA Computing and Molecular Programming - 25th International Conference, DNA 25, Proceedings. red. / Yan Liu; Chris Thachuk. Springer, 2019. s. 54-79 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11648 LNCS).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Singh, A
, Wiuf, C, Behera, A & Gopalkrishnan, M 2019,
A Reaction Network Scheme Which Implements Inference and Learning for Hidden Markov Models. i Y Liu & C Thachuk (red),
DNA Computing and Molecular Programming - 25th International Conference, DNA 25, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 11648 LNCS, s. 54-79, 25th International Conference on DNA Computing and Molecular Programming, DNA 2019, Seattle, USA,
05/08/2019.
https://doi.org/10.1007/978-3-030-26807-7_4APA
Singh, A.
, Wiuf, C., Behera, A., & Gopalkrishnan, M. (2019).
A Reaction Network Scheme Which Implements Inference and Learning for Hidden Markov Models. I Y. Liu, & C. Thachuk (red.),
DNA Computing and Molecular Programming - 25th International Conference, DNA 25, Proceedings (s. 54-79). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Bind 11648 LNCS
https://doi.org/10.1007/978-3-030-26807-7_4Vancouver
Singh A
, Wiuf C, Behera A, Gopalkrishnan M.
A Reaction Network Scheme Which Implements Inference and Learning for Hidden Markov Models. I Liu Y, Thachuk C, red., DNA Computing and Molecular Programming - 25th International Conference, DNA 25, Proceedings. Springer. 2019. s. 54-79. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11648 LNCS).
https://doi.org/10.1007/978-3-030-26807-7_4Author
Singh, Abhinav ; Wiuf, Carsten ; Behera, Abhishek ; Gopalkrishnan, Manoj. / A Reaction Network Scheme Which Implements Inference and Learning for Hidden Markov Models. DNA Computing and Molecular Programming - 25th International Conference, DNA 25, Proceedings. red. / Yan Liu ; Chris Thachuk. Springer, 2019. s. 54-79 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11648 LNCS).
Bibtex
@inproceedings{aaad50d2a8f84dcc9d227dc42bb17849,
title = "A Reaction Network Scheme Which Implements Inference and Learning for Hidden Markov Models",
abstract = "With a view towards molecular communication systems and molecular multi-agent systems, we propose the Chemical Baum-Welch Algorithm, a novel reaction network scheme that learns parameters for Hidden Markov Models (HMMs). Each reaction in our scheme changes only one molecule of one species to one molecule of another. The reverse change is also accessible but via a different set of enzymes, in a design reminiscent of futile cycles in biochemical pathways. We show that every fixed point of the Baum-Welch algorithm for HMMs is a fixed point of our reaction network scheme, and every positive fixed point of our scheme is a fixed point of the Baum-Welch algorithm. We prove that the {\^a}€{\oe}Expectation{\^a}€� step and the {\^a}€{\oe}Maximization{\^a}€� step of our reaction network separately converge exponentially fast. We simulate mass-action kinetics for our network on an example sequence, and show that it learns the same parameters for the HMM as the Baum-Welch algorithm.",
author = "Abhinav Singh and Carsten Wiuf and Abhishek Behera and Manoj Gopalkrishnan",
year = "2019",
doi = "10.1007/978-3-030-26807-7_4",
language = "English",
isbn = "9783030268060",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "54--79",
editor = "Yan Liu and Chris Thachuk",
booktitle = "DNA Computing and Molecular Programming - 25th International Conference, DNA 25, Proceedings",
address = "Switzerland",
note = "25th International Conference on DNA Computing and Molecular Programming, DNA 2019 ; Conference date: 05-08-2019 Through 09-08-2019",
}
RIS
TY - GEN
T1 - A Reaction Network Scheme Which Implements Inference and Learning for Hidden Markov Models
AU - Singh, Abhinav
AU - Wiuf, Carsten
AU - Behera, Abhishek
AU - Gopalkrishnan, Manoj
PY - 2019
Y1 - 2019
N2 - With a view towards molecular communication systems and molecular multi-agent systems, we propose the Chemical Baum-Welch Algorithm, a novel reaction network scheme that learns parameters for Hidden Markov Models (HMMs). Each reaction in our scheme changes only one molecule of one species to one molecule of another. The reverse change is also accessible but via a different set of enzymes, in a design reminiscent of futile cycles in biochemical pathways. We show that every fixed point of the Baum-Welch algorithm for HMMs is a fixed point of our reaction network scheme, and every positive fixed point of our scheme is a fixed point of the Baum-Welch algorithm. We prove that the “Expectation� step and the “Maximization� step of our reaction network separately converge exponentially fast. We simulate mass-action kinetics for our network on an example sequence, and show that it learns the same parameters for the HMM as the Baum-Welch algorithm.
AB - With a view towards molecular communication systems and molecular multi-agent systems, we propose the Chemical Baum-Welch Algorithm, a novel reaction network scheme that learns parameters for Hidden Markov Models (HMMs). Each reaction in our scheme changes only one molecule of one species to one molecule of another. The reverse change is also accessible but via a different set of enzymes, in a design reminiscent of futile cycles in biochemical pathways. We show that every fixed point of the Baum-Welch algorithm for HMMs is a fixed point of our reaction network scheme, and every positive fixed point of our scheme is a fixed point of the Baum-Welch algorithm. We prove that the “Expectation� step and the “Maximization� step of our reaction network separately converge exponentially fast. We simulate mass-action kinetics for our network on an example sequence, and show that it learns the same parameters for the HMM as the Baum-Welch algorithm.
U2 - 10.1007/978-3-030-26807-7_4
DO - 10.1007/978-3-030-26807-7_4
M3 - Article in proceedings
AN - SCOPUS:85070695913
SN - 9783030268060
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 54
EP - 79
BT - DNA Computing and Molecular Programming - 25th International Conference, DNA 25, Proceedings
A2 - Liu, Yan
A2 - Thachuk, Chris
PB - Springer
T2 - 25th International Conference on DNA Computing and Molecular Programming, DNA 2019
Y2 - 5 August 2019 through 9 August 2019
ER -
