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 |
|
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 |
|
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 |
|