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人工智能

SSCI 2019

IEEE Symposium Series on Computational Intelligence

全文截稿: 2019-07-10

开会时间: 2019-12-06

会议难度: ★★

CCF分类: 无

会议地点: Xiamen, China

网址：http://www.ssci2019.org

The 2019 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2019) will be held in a very beautiful port city, Xiamen. It is on China’s southeast coast, across a strait from Taiwan. It encompasses 2 main islands and a region on the mainland. Formerly known as Amoy, it was a British-run treaty port from 1842 to 1912. Many Europeans and Japanese lived on Gulangyu, today a vehicle-free island with beaches and meandering streets lined with old colonial villas.

IEEE SSCI is a flagship annual international conference on computational intelligence sponsored by the IEEE Computational Intelligence Society, promoting all aspects of theory, algorithm design, applications and related emerging techniques. As a tradition, IEEE SSCI 2019 will co-locate a large number of exciting symposiums, each dedicated to a special topic within or related to computational intelligence, thereby providing a unique platform for promoting cross-fertilization and collaboration. IEEE SSCI 2019 will be featured by keynote speeches, panel discussions, oral presentations and poster sessions.

Looking forward to seeing you at SSCI2019 and wish you have wonderful time in Xiamen!

人工智能

ISRL 2019

International Symposium on Reinforcement Learning

全文截稿: 2019-09-01

开会时间: 2019-12-10

会议难度: ★★

CCF分类: 无

会议地点: Tokyo, Japan

网址：http://www.cloud-conf.net/ISRL/2019/isrl.html

IEEE ISRL 2019 aims to collect recent academic achievements in novel techniques, developments, empirical studies, and new developments in reinforcement learning. Innovative technical applications based on reinforcement learning algorithms are highly encouraged. The objective of IEEE ISRL 2019 is to provide a forum for scientists, engineers, and researchers to discuss and exchange their new ideas, novel results, work in progress and experience on all aspects of reinforcement learning. Topics of particular interest include, but are not limited to:

Current state of reinforcement learning algorithms
Technical issues of reinforcement learning applications
Theoretical and experimental analysis of reinforcement learning
Security and Privacy with reinforcement learning
Learning paradigms for reinforcement learning
Reinforcement learning for scheduling
Reinforcement learning based behaviour correction
High performance computing for training with reinforcement learning
The future applications of reinforcement learning

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