In lifelong learning, data are used to improve performance not only on the present task, but also on past and future (unencountered) tasks. While typical transfer learning algorithms can improve performance on future tasks, their performance on prior tasks degrades upon learning new tasks (called fo...
Détails bibliographiques
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 47(2025), 11 vom: 01. Okt., Seite 10033-10046
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| Auteur principal: |
Vogelstein, Joshua T
(Auteur) |
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| Autres auteurs: |
Dey, Jayanta,
Helm, Hayden S,
LeVine, Will,
Mehta, Ronak D,
Tomita, Tyler M,
Xu, Haoyin,
Geisa, Ali,
Wang, Qingyang,
van de Ven, Gido M,
Gao, Chenyu,
Yang, Weiwei,
Tower, Bryan,
Larson, Jonathan,
White, Christopher M,
Priebe, Carey E |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2025
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence
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| Sujets: | Journal Article |
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