18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019

Description

A fundamental task for Multi-Agent Systems (MAS) is learning. Deep Neural Networks (DNNs) have proven to cope perfectly with all learning paradigms, i.e. supervised, unsupervised, and reinforcement learning. Nevertheless, traditional deep learning approaches make use of cloud computing facilities and do not scale well to autonomous agents with low computational resources. Even in the cloud they suffer from computational and memory limitations and cannot be used to model properly large physical worlds for agents which assume networks with billion of neurons. These issues were addressed in the last few years by the emerging topics of scalable and efficient deep learning. The tutorial covers these topics focusing on theoretical advancements, practical applications, and hands-on experience.

Period	13 May 2019
Event type	Conference
Conference number	18
Location	Montreal, Canada, QuebecShow on map
Degree of Recognition	International