bibtype V - Research Report
ARLID 0538247
utime 20240103225228.1
mtime 20210121235959.9
title (primary) (eng) Bayesian transfer learning between autoregressive inference tasks
publisher
place Praha
name ÚTIA AV ČR
pub_time 2020
specification
media_type P
edition
name Research Report
volume_id 2389
keyword autoregression
keyword transfer learning
keyword Fully Probabilistic Design
keyword FPD
keyword food-commodities price prediction
author (primary)
ARLID cav_un_auth*0403479
name1 Barber
name2 Alec
institution UTIA-B
full_dept (cz) Adaptivní systémy
full_dept (eng) Department of Adaptive Systems
department (cz) AS
department (eng) AS
country IE
garant S
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/quinn-0538247.pdf
cas_special
project
project_id GA18-15970S
agency GA ČR
country CZ
ARLID cav_un_auth*0362986
abstract (eng) Bayesian transfer learning typically relies on a complete stochastic dependence speci cation between source and target learners which allows the opportunity for Bayesian conditioning. We advocate that any requirement for the design or assumption of a full model between target and sources is a restrictive form of transfer learning.
RIV BD
FORD0 10000
FORD1 10100
FORD2 10102
reportyear 2021
num_of_auth 2
mrcbC52 4 O 4o 20231122145512.8
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0316079
confidential S
arlyear 2020
mrcbTft \nSoubory v repozitáři: 0538247.pdf
mrcbU10 2020
mrcbU10 Praha ÚTIA AV ČR