Year: 2025
Type: conference-paper
Abstract: Deep learning techniques, such as large language models and graph neural networks, have demonstrated impressive effectiveness across various web applications. Despite their success, the advancement of these methods is frequently hampered by different resource constraint challenges. Key challenges include the scarcity of labeled data (data-level constraints), the need for smaller model sizes that fit real-world computing environments (model-level constraints), and the integration of neural network design with system and hardware for energy efficiency (system-level constraints). Tackling these issues is essential for the effective and efficient deployment of models in various real-world web systems and applications, including social networks, search engines, recommender systems, question answering, and content analysis. Therefore, there is an urgent need to develop innovative and efficient learning techniques that can overcome these resource limitations. The proposed international workshop on ''Resource-Efficient Learning for the Web (RelWeb 2025)'' will provide a great venue for academic researchers and industrial practitioners to share challenges, solutions, and future opportunities for resource-efficient learning. Our workshop objectives are threefold: (1) to establish an engaging platform where experts and participants can share their latest research findings and innovative practices in resource-efficient learning; (2) to explore emerging technologies and trends that could shape the future of this field; and (3) to foster a collaborative environment that encourages partnerships and exchanges among participants. Together, we can advance resource-efficient learning and propel future research in this area.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4410636337