bibtype M - Monography Chapter
ARLID 0537103
utime 20240103225051.3
mtime 20210107235959.9
DOI 10.1007/978-3-030-63193-2_9
title (primary) (eng) Bayesian transfer learning between uniformly modelled Bayesian filters
specification
page_count 18 s.
book_pages 193
media_type P
serial
ARLID cav_un_epca*0537102
ISBN 978-3-030-63192-5
title Informatics in Control, Automation and Robotics : 16th International Conference, ICINCO 2019 Prague, Czech Republic, July 29-31, 2019, Revised Selected Papers
page_num 151-168
publisher
place Cham
name Springer
year 2021
editor
name1 Gusikhin
name2 Oleg
editor
name1 Madani
name2 Kurosh
editor
name1 Zaytoon
name2 Janan
keyword Bayesian transfer learning
keyword Fully probabilistic design
keyword Bayesian filtering
keyword Uniform noise
keyword Parallelotopic bounds
author (primary)
ARLID cav_un_auth*0101119
name1 Jirsa
name2 Ladislav
institution UTIA-B
full_dept (cz) Adaptivní systémy
full_dept (eng) Department of Adaptive Systems
department (cz) AS
department (eng) AS
full_dept Department of Adaptive Systems
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0382598
name1 Kuklišová Pavelková
name2 Lenka
institution UTIA-B
full_dept (cz) Adaptivní systémy
full_dept Department of Adaptive Systems
department (cz) AS
department AS
full_dept Department of Adaptive Systems
country CZ
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0370768
name1 Quinn
name2 Anthony
institution UTIA-B
full_dept (cz) Adaptivní systémy
full_dept Department of Adaptive Systems
department (cz) AS
department AS
full_dept Department of Adaptive Systems
country IE
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2021/AS/kuklisova-0537103.pdf
cas_special
project
project_id GA18-15970S
agency GA ČR
country CZ
ARLID cav_un_auth*0362986
abstract (eng) We investigate sensor network nodes that sequentially infer states with bounded values, and affected by noise that is also bounded. The transfer of knowledge between such nodes is the principal focus of this chapter. A fully Bayesian framework is adopted, in which the source knowledge is represented by a bounded data predictor, the specification of a formal conditioning mechanism between the filtering nodes is avoided, and the optimal knowledge-conditioned target state predictor is designed via optimal Bayesian decision-making (fully\nprobabilistic design). We call this framework Bayesian transfer learning, and derive a sequential algorithm for pairs of interacting, bounded filters. To achieve a tractable, finite-dimensional flow, the outputs of the time step, transfer step and data step are locally projected onto parallelotopic supports. An informal variant of the transfer algorithm demonstrates both strongly positive transfer of high-quality (low variance) source knowledge--improving on a former orthotopically supported variant--as well as rejection of low-quality (high variance) source knowledge, which we call robust transfer.
reportyear 2022
RIV BC
FORD0 10000
FORD1 10200
FORD2 10201
num_of_auth 3
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0315009
confidential S
arlyear 2021
mrcbU14 SCOPUS
mrcbU24 PUBMED
mrcbU34 WOS
mrcbU63 cav_un_epca*0537102 Informatics in Control, Automation and Robotics : 16th International Conference, ICINCO 2019 Prague, Czech Republic, July 29-31, 2019, Revised Selected Papers 978-3-030-63192-5 151 168 Cham Springer 2021 Lecture Notes in Electrical Engineering 720
mrcbU67 Gusikhin Oleg 340
mrcbU67 Madani Kurosh 340
mrcbU67 Zaytoon Janan 340