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Hypernetwork approach to Bayesian MAML (Student Abstract)
Author
Editor
Walsh Toby
Shah Julie
Kolter Zico
Volume
39
Book title / Journal title
Proceedings of the 39th AAAI Conference on Artificial Intelligence
Place
Washington
Publisher
AAAI Press
Volume title
Proceedings of the 39th Annual AAAI Conference on Artificial IntelligenceIAAI-25, EAAI-25, AAAI-25 Student Abstracts, Undergraduate Consortium and Demonstrations
Pages
29325-29327
ISBN
978-1-57735-897-81-57735-897-X
Serie's ISSN
2159-5399
Serie's eISSN
2374-3468
Language
English
Book language / Journal language
English
Abstract in English
The main goal of Few-Shot learning algorithms is to enabl learning from small amounts of data. One of the most popular and elegant Few-Shot learning approaches is Model-Agnostic Meta-Learning (MAML). In this paper, we propose a novel framework for Bayesian MAML called BH-MAML, which employs Hypernetworks for weight updates. It learns the universal weights point-wise, but a probabilistic structure is added when adapted for specific tasks. In such a framework, we can use simple Gaussian distributions or more complicated posteriors induced by Continuous Normalizing Flows
Conference
39th AAAI Conference on Artificial Intelligence
Conference short name
AAAI-25
Conference start date
2025-02-25
End date conference
2025-03-04
Conference city
Filadelfia, Pensylwania
Conference country
Stany Zjednoczone
Conference type
international
Affiliation
Szkoła Doktorska Nauk Ścisłych i PrzyrodniczychWydział Matematyki i Informatyki : Instytut Informatyki i Matematyki Komputerowej
| dc.abstract.en | The main goal of Few-Shot learning algorithms is to enabl learning from small amounts of data. One of the most popular and elegant Few-Shot learning approaches is Model-Agnostic Meta-Learning (MAML). In this paper, we propose a novel framework for Bayesian MAML called BH-MAML, which employs Hypernetworks for weight updates. It learns the universal weights point-wise, but a probabilistic structure is added when adapted for specific tasks. In such a framework, we can use simple Gaussian distributions or more complicated posteriors induced by Continuous Normalizing Flows | |
| dc.affiliation | Szkoła Doktorska Nauk Ścisłych i Przyrodniczych | |
| dc.affiliation | Wydział Matematyki i Informatyki : Instytut Informatyki i Matematyki Komputerowej | |
| dc.conference | 39th AAAI Conference on Artificial Intelligence | |
| dc.conference.city | Filadelfia, Pensylwania | |
| dc.conference.country | Stany Zjednoczone | |
| dc.conference.datefinish | 2025-03-04 | |
| dc.conference.datestart | 2025-02-25 | |
| dc.conference.series | National Conference of the American Association for Artificial Intelligence | |
| dc.conference.seriesshortcut | AAAI | |
| dc.conference.shortcut | AAAI-25 | |
| dc.conference.weblink | https://aaai.org/conference/aaai/aaai-25/ | |
| dc.contributor.author | Borycki, Piotr | |
| dc.contributor.author | Kubacki, Piotr | |
| dc.contributor.author | Przewięźlikowski, Marcin - 421101 | |
| dc.contributor.author | Kuśmierczyk, Tomasz - 498199 | |
| dc.contributor.author | Tabor, Jacek - 132362 | |
| dc.contributor.author | Spurek, Przemysław - 135993 | |
| dc.contributor.editor | Walsh, Toby | |
| dc.contributor.editor | Shah, Julie | |
| dc.contributor.editor | Kolter, Zico | |
| dc.date.accessioned | 2025-05-19T06:33:38Z | |
| dc.date.available | 2025-05-19T06:33:38Z | |
| dc.date.createdat | 2025-04-15T07:48:22Z | en |
| dc.date.issued | 2025 | |
| dc.date.openaccess | 0 | |
| dc.description.accesstime | w momencie opublikowania | |
| dc.description.conftype | international | |
| dc.description.physical | 29325-29327 | |
| dc.description.version | ostateczna wersja wydawcy | |
| dc.description.volume | 39 | |
| dc.identifier.bookweblink | https://search.worldcat.org/title/10789238955?oclcNum=10789238955 | |
| dc.identifier.doi | 10.1609/aaai.v39i28.35239 | |
| dc.identifier.isbn | 978-1-57735-897-8 | |
| dc.identifier.isbn | 1-57735-897-X | |
| dc.identifier.project | 2021/41/B/ST6/01370 | |
| dc.identifier.project | 2023/50/E/ST6/00068 | |
| dc.identifier.project | 2023/49/N/ST6/03268 | |
| dc.identifier.project | 2022/45/P/ST6/0296 | |
| dc.identifier.project | Marie Sklodowska-Curie grant agreement No. 945339 | |
| dc.identifier.serieseissn | 2374-3468 | |
| dc.identifier.seriesissn | 2159-5399 | |
| dc.identifier.uri | https://ruj.uj.edu.pl/handle/item/552534 | |
| dc.language | eng | |
| dc.language.container | eng | |
| dc.place | Washington | |
| dc.publisher | AAAI Press | |
| dc.rights | Dodaję tylko opis bibliograficzny | |
| dc.rights.licence | Inna otwarta licencja | |
| dc.share.type | inne | |
| dc.source.integrator | false | |
| dc.subtype | ConferenceProceedings | |
| dc.title | Hypernetwork approach to Bayesian MAML (Student Abstract) | |
| dc.title.container | Proceedings of the 39th AAAI Conference on Artificial Intelligence | |
| dc.title.volume | Proceedings of the 39th Annual AAAI Conference on Artificial IntelligenceIAAI-25, EAAI-25, AAAI-25 Student Abstracts, Undergraduate Consortium and Demonstrations | |
| dc.type | BookSection | |
| dspace.entity.type | Publication | en |
dc.abstract.en
The main goal of Few-Shot learning algorithms is to enabl learning from small amounts of data. One of the most popular and elegant Few-Shot learning approaches is Model-Agnostic Meta-Learning (MAML). In this paper, we propose a novel framework for Bayesian MAML called BH-MAML, which employs Hypernetworks for weight updates. It learns the universal weights point-wise, but a probabilistic structure is added when adapted for specific tasks. In such a framework, we can use simple Gaussian distributions or more complicated posteriors induced by Continuous Normalizing Flows dc.affiliation
Szkoła Doktorska Nauk Ścisłych i Przyrodniczych dc.affiliation
Wydział Matematyki i Informatyki : Instytut Informatyki i Matematyki Komputerowej dc.conference
39th AAAI Conference on Artificial Intelligence dc.conference.city
Filadelfia, Pensylwania dc.conference.country
Stany Zjednoczone dc.conference.datefinish
2025-03-04 dc.conference.datestart
2025-02-25 dc.conference.series
National Conference of the American Association for Artificial Intelligence dc.conference.seriesshortcut
AAAI dc.conference.shortcut
AAAI-25 dc.conference.weblink
https://aaai.org/conference/aaai/aaai-25/ dc.contributor.author
Borycki, Piotr dc.contributor.author
Kubacki, Piotr dc.contributor.author
Przewięźlikowski, Marcin - 421101 dc.contributor.author
Kuśmierczyk, Tomasz - 498199 dc.contributor.author
Tabor, Jacek - 132362 dc.contributor.author
Spurek, Przemysław - 135993 dc.contributor.editor
Walsh, Toby dc.contributor.editor
Shah, Julie dc.contributor.editor
Kolter, Zico dc.date.accessioned
2025-05-19T06:33:38Z dc.date.available
2025-05-19T06:33:38Z dc.date.createdaten
2025-04-15T07:48:22Z dc.date.issued
2025 dc.date.openaccess
0 dc.description.accesstime
w momencie opublikowania dc.description.conftype
international dc.description.physical
29325-29327 dc.description.version
ostateczna wersja wydawcy dc.description.volume
39 dc.identifier.bookweblink
https://search.worldcat.org/title/10789238955?oclcNum=10789238955 dc.identifier.doi
10.1609/aaai.v39i28.35239 dc.identifier.isbn
978-1-57735-897-8 dc.identifier.isbn
1-57735-897-X dc.identifier.project
2021/41/B/ST6/01370 dc.identifier.project
2023/50/E/ST6/00068 dc.identifier.project
2023/49/N/ST6/03268 dc.identifier.project
2022/45/P/ST6/0296 dc.identifier.project
Marie Sklodowska-Curie grant agreement No. 945339 dc.identifier.serieseissn
2374-3468 dc.identifier.seriesissn
2159-5399 dc.identifier.uri
https://ruj.uj.edu.pl/handle/item/552534 dc.language
eng dc.language.container
eng dc.place
Washington dc.publisher
AAAI Press dc.rights
Dodaję tylko opis bibliograficzny dc.rights.licence
Inna otwarta licencja dc.share.type
inne dc.source.integrator
false dc.subtype
ConferenceProceedings dc.title
Hypernetwork approach to Bayesian MAML (Student Abstract) dc.title.container
Proceedings of the 39th AAAI Conference on Artificial Intelligence dc.title.volume
Proceedings of the 39th Annual AAAI Conference on Artificial IntelligenceIAAI-25, EAAI-25, AAAI-25 Student Abstracts, Undergraduate Consortium and Demonstrations dc.type
BookSection dspace.entity.typeen
Publication Affiliations
Wydział Matematyki i Informatyki
Borycki, Piotr
computer and information sciences
Kubacki, Piotr
computer and information sciences
Przewięźlikowski, Marcin
Kuśmierczyk, Tomasz
information and communication technology
Tabor, Jacek
information and communication technology
Spurek, Przemysław
information and communication technology
Szkoła Doktorska Nauk Ścisłych i Przyrodniczych
Przewięźlikowski, Marcin
No affiliation
Walsh, Toby
Shah, Julie
Kolter, Zico
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