Correction to: Comparing and Improving Active Learning Uncertainty Measures for Transformer Models by Discarding Outliers

IF 6.9 3区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems Frontiers Pub Date : 2024-07-15 DOI:10.1007/s10796-024-10519-5

Julius Gonsior, Christian Falkenberg, Silvio Magino, Anja Reusch, Claudio Hartmann, Maik Thiele, Wolfgang Lehner

引用次数: 0

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更正：通过剔除异常值来比较和改进变压器模型的主动学习不确定性测量方法

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Information Systems Frontiers 工程技术-计算机：理论方法

CiteScore

13.30

自引率

18.60%

发文量

127

审稿时长

9 months

期刊介绍： The interdisciplinary interfaces of Information Systems (IS) are fast emerging as defining areas of research and development in IS. These developments are largely due to the transformation of Information Technology (IT) towards networked worlds and its effects on global communications and economies. While these developments are shaping the way information is used in all forms of human enterprise, they are also setting the tone and pace of information systems of the future. The major advances in IT such as client/server systems, the Internet and the desktop/multimedia computing revolution, for example, have led to numerous important vistas of research and development with considerable practical impact and academic significance. While the industry seeks to develop high performance IS/IT solutions to a variety of contemporary information support needs, academia looks to extend the reach of IS technology into new application domains. Information Systems Frontiers (ISF) aims to provide a common forum of dissemination of frontline industrial developments of substantial academic value and pioneering academic research of significant practical impact.