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Multi-Criteria Evaluation of Publication Impacts: Deep Learning in Autonomous Vehicles

机译：出版物影响的多标准评估：自治车辆深度学习

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摘要

Deep learning is the state-of-the-art approach that has been extensively used in the recent years to variety of real-world problems in the literature. The autonomous vehicles are among the applications where their integration with deep learning techniques has potential to disruptively change our daily lives. In this work, we have proposed a multi-criteria framework to evaluate the relative impacts of both publications and authors for deep learning in autonomous vehicles. For the framework, we have considered several criteria extracted from the metadata of the publications and the authors. The conflicts among the criteria are also justified through Pearson correlation. For the experiments, two comprehensive datasets for the publication and the author impacts have been constructed. The resulting pareto-fronts of the datasets after ranking are presented. Moreover, top 30 most impactful publications and authors in the literature are identified. We hope that our findings will be useful for researchers to accelerate the further technological advancements.
机译：深度学习是最先进的方法，在近年来，在文献中的实际问题各种各样地广泛使用。自治车辆是与深入学习技术的整合的应用中，有可能破坏我们的日常生活。在这项工作中，我们提出了一个多标准框架，以评估出版物和作者在自动车辆中深入学习的相对影响。对于框架，我们考虑了从出版物和作者的元数据中提取了几个标准。标准之间的冲突也通过Pearson相关性。对于实验，已经建立了两个出版物和作者影响的全面数据集。在排名后，将产生的数据集的据占用。此外，鉴定了文献中最多30个最有影响力的出版物和作者。我们希望我们的调查结果对于研究人员来说是有用的，以加速进一步的技术进步。

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