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Coles Generalizing from Limited Resources the Open World: Third International Workshop, GLOW 2025, Held Conjunction with IJCAI Montreal, Canada, August 16-22, Proceedings at Bayshore Shopping Centre in Ottawa, ON
Generalizing from Limited Resources the Open World: Third International Workshop, GLOW 2025, Held Conjunction with IJCAI Montreal, Canada, August 16-22, Proceedings
Generalizing from Limited Resources the Open World: Third International Workshop, GLOW 2025, Held Conjunction with IJCAI Montreal, Canada, August 16-22, Proceedings in Ottawa, ON
Generalizing from Limited Resources the Open World: Third International Workshop, GLOW 2025, Held Conjunction with IJCAI Montreal, Canada, August 16-22, Proceedings in Ottawa, ON
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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.
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.
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