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Simone Parisi, Davide Tateo, Maximilian Hensel, Carlo D?Eramo, Jan Peters and Joni Pajarinen
Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getting feedback via extrinsic rewards to train the agent, and in situations where this occurs very rarely the agent learns slowly or cannot learn at all. Simi...
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Yu Yao and Quan Qian
We develop the online process parameter design (OPPD) framework for efficiently handling streaming data collected from industrial automation equipment. This framework integrates online machine learning, concept drift detection and Bayesian optimization t...
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S. Bhuvaneeswari,B. Ramesh Kumar,R. Solaiappan,S. Murugesan
This paper, we develops a nonlinear programming approach to construct the membership function of the performance measure in bulk arrival queueing system , in that the arrival and service rates are fuzzy numbers. The basic idea is transform to a ?Generali...
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Peng Wang, Jingju Liu, Dongdong Hou and Shicheng Zhou
The application of cybersecurity knowledge graphs is attracting increasing attention. However, many cybersecurity knowledge graphs are incomplete due to the sparsity of cybersecurity knowledge. Existing knowledge graph completion methods do not perform w...
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Elena Gangan,Milos Kudus,Eugene Ilyushin
Pág. 12 - 27
The main goal of this paper is to introduce the reader to the multiarmed bandit algorithms of different types and to observe how the industry leveraged them in advancing recommendation systems. We present the current state of the art in RecSys and then ...
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