Generalizing from Limited Resources in the Open World (eBook, PDF)
Third International Workshop, GLOW 2025, Held in Conjunction with IJCAI 2025, Montreal, Canada, August 16-22, 2025, Proceedings
Redaktion: Ma, Yuqing; Liu, Xianglong; Ning, Xuefei; Liu, Ning; Gong, Ruihao; Zhao, Xiaowei; Guo, Jinyang
Versandkostenfrei!
Sofort per Download lieferbar
48,95 €
inkl. MwSt.
Weitere Ausgaben:
PAYBACK Punkte
24 °P sammeln!
This book presents the proceedings from the Third International Workshop on Generalizing from Limited Resources in the Open World (GLOW) 2025 held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI 2025, in Montreal, Canada, during August 16-22, 2025.The 12 full papers in this book were carefully reviewed and selected from 27 submissions. These papers focus on the academic exploration of efficient methodologies within the realm of artificial intelligence models. We concentrated on both data-efficient strategies, such as zero/few-shot learning and domain ad...
This book presents the proceedings from the Third International Workshop on Generalizing from Limited Resources in the Open World (GLOW) 2025 held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI 2025, in Montreal, Canada, during August 16-22, 2025.
The 12 full papers in this book were carefully reviewed and selected from 27 submissions. These papers focus on the academic exploration of efficient methodologies within the realm of artificial intelligence models. We concentrated on both data-efficient strategies, such as zero/few-shot learning and domain adaptation, as well as model-efficient approaches like model sparsification and compact model design.
The 12 full papers in this book were carefully reviewed and selected from 27 submissions. These papers focus on the academic exploration of efficient methodologies within the realm of artificial intelligence models. We concentrated on both data-efficient strategies, such as zero/few-shot learning and domain adaptation, as well as model-efficient approaches like model sparsification and compact model design.
Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.
Andere Kunden interessierten sich für
