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    Multiagent Learning
    Encyclopedia of the Sciences of Learning 2012-01-01 Résumé à partir de l’abstract
    Multiagent Learning System
    Encyclopedia of the Sciences of Learning 2012-01-01 Résumé à partir de l’abstract
    Multiagent Traffic Management: Opportunities for Multiagent Learning
    Kurt Dresner, Peter Stone
    Lecture Notes in Computer Science 2006-01-01 Résumé à partir de l’abstract
    Convergence, Targeted Optimality and Safety in Multiagent Learning
    Doran Chakraborty
    Studies in Computational Intelligence 2014-01-01 Résumé à partir de l’abstract
    Multiagent simulation of learning environments
    Elizabeth Sklar, Mathew Davies
    Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems 2005-07-25 Résumé à partir de l’abstract
    Adaptive policy gradient in multiagent learning
    Bikramjit Banerjee, Jing Peng
    Proceedings of the second international joint conference on Autonomous agents and multiagent systems 2003-07-14 Résumé à partir de l’abstract
    Advice taking in multiagent reinforcement learning
    Michael Rovatsos, Alexandros Belesiotis
    Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems 2007-05-14 Résumé à partir de l’abstract
    Deep learning in multiagent systems
    Lukas Esterle
    Deep Learning for Robot Perception and Cognition 2022-01-01 Résumé à partir de l’abstract
    Multiagent learning in adaptive dynamic systems
    Andriy Burkov, Brahim Chaib-draa
    Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems 2007-05-14 Résumé à partir de l’abstract
    Optimizing deep learning models: A review
    Abdelkader Belhadri, Ibtissam Benchennane

    Recent advances in deep learning have enabled the development of highly powerful models, but these models are also extremely resource-intensive. This results in high financial costs, prolonged training times, and a significant environmental impact. Consequently, optimizing the training process is …

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    Recent advances in deep learning have enabled the development of highly powerful models, but these models are also extremely resource-intensive. This results in high financial costs, prolonged training times, and a significant environmental impact. Consequently, optimizing the training process is essential to mitigate these costs while maintaining or enhancing performance. In this context, software-level optimization plays a crucial role by improving training pipelines, memory management, and overall code efficiency. Fine-tuning hyperparameters, as part of this code optimization process, is the focus of this review, which examines how to effectively combine and adjust these parameters to boost overall model performance while reducing both costs and environmental impact.

    Multiagent and Grid Systems 2025-05-01 Résumé à partir de l’abstract