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Multiagent Learning

Encyclopedia of the Sciences of Learning 2012-01-01 Summary from the abstract

Multiagent Learning System

Encyclopedia of the Sciences of Learning 2012-01-01 Summary from the abstract

Multiagent Traffic Management: Opportunities for Multiagent Learning

Kurt Dresner, Peter Stone

Lecture Notes in Computer Science 2006-01-01 Summary from the abstract

Convergence, Targeted Optimality and Safety in Multiagent Learning

Doran Chakraborty

Studies in Computational Intelligence 2014-01-01 Summary from the 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 Summary from the 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 Summary from the 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 Summary from the abstract

Deep learning in multiagent systems

Lukas Esterle

Deep Learning for Robot Perception and Cognition 2022-01-01 Summary from the 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 Summary from the 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 Summary from the abstract