Generalizing from Limited Resources in the Open World

Beschreibung

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.

Autor*in

Yuqing Ma

Themen in »Generalizing from Limited Resources in the Open World«

Artificial Intelligence Deep Learning Efficient Methods Few/Zero-shot Learning Open Set/World Learning Domain-adaptation methods On-Device Deployment Brain-inspired AI Applications of AI Models Data-efficient Model Optimization and Training Techniques

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