| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0510186 |
| utime |
20240103222810.7 |
| mtime |
20191031235959.9 |
| SCOPUS |
85077706875 |
| DOI |
10.1109/MLSP.2019.8918783 |
| title
(primary) (eng) |
Robust Bayesian transfer learning between Kalman filters |
| specification |
| page_count |
6 s. |
| media_type |
C |
|
| serial |
| ARLID |
cav_un_epca*0517233 |
| ISBN |
978-1-7281-0824-7 |
| title
|
PROCEEDINGS OF MLSP 2019 : IEEE 29th International Workshop on Machine Learning for Signal Processing |
| publisher |
| place |
Piscataway |
| name |
IEEE |
| year |
2019 |
|
|
| keyword |
Bayesian transfer learning |
| keyword |
Robust knowledge transfer |
| keyword |
Scalar relaxation |
| keyword |
Fully probabilistic design |
| keyword |
Kalman filtering |
| author
(primary) |
| ARLID |
cav_un_auth*0370767 |
| name1 |
Papež |
| name2 |
Milan |
| 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 |
| 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 |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0362986 |
| project_id |
GA18-15970S |
| agency |
GA ČR |
| country |
CZ |
|
| abstract
(eng) |
Bayesian transfer learning typically requires complete specification of the stochastic dependence between source and target domains. Fully probabilistic design-based Bayesian transfer learning---which transfers source knowledge in the form of a probability distribution-obviates these restrictive assumptions. However, this approach has suffered from negative transfer when the source knowledge is imprecise. We propose a scale variable relaxation to transfer all source moments successfully, achieving robust transfer (i.e. rejection of imprecise source knowledge). A recursive algorithm is recovered via local variational Bayes approximation. The solution offers positive transfer of precise source knowledge, while rejecting it when imprecise. Experiments show that the technique is competitive with or equivalent to alternative methods. |
| action |
| ARLID |
cav_un_auth*0383979 |
| name |
IEEE 29th International Workshop on Machine Learning for Signal Processing |
| dates |
20191013 |
| mrcbC20-s |
20191016 |
| place |
Pittsburgh |
| country |
US |
|
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2020 |
| num_of_auth |
2 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0302515 |
| mrcbC61 |
1 |
| confidential |
S |
| article_num |
19 |
| arlyear |
2019 |
| mrcbU14 |
85077706875 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0517233 PROCEEDINGS OF MLSP 2019 : IEEE 29th International Workshop on Machine Learning for Signal Processing 978-1-7281-0824-7 Piscataway IEEE 2019 |
|