论文标题
机器学习最先进的不确定性
Machine Learning State-of-the-Art with Uncertainties
论文作者
论文摘要
随着数据，硬件，软件生态系统和相关技能的可用性，机器学习社区每年都以高频出现新的体系结构和方法正在快速发展。在本文中，我们进行了一项模范图像分类研究，以证明围绕准确性测量的置信区间如何极大地增强研究结果的交流并影响审查过程。此外，我们探讨了此近似值的标志和局限性。我们讨论了这种方法的相关性，以反映ICLR22的聚光灯出版物。可再现的工作流程可作为本出版物的开源伴随。根据我们的讨论，我们提出了改善机器学习文章的创作和审查过程的建议。
With the availability of data, hardware, software ecosystem and relevant skill sets, the machine learning community is undergoing a rapid development with new architectures and approaches appearing at high frequency every year. In this article, we conduct an exemplary image classification study in order to demonstrate how confidence intervals around accuracy measurements can greatly enhance the communication of research results as well as impact the reviewing process. In addition, we explore the hallmarks and limitations of this approximation. We discuss the relevance of this approach reflecting on a spotlight publication of ICLR22. A reproducible workflow is made available as an open-source adjoint to this publication. Based on our discussion, we make suggestions for improving the authoring and reviewing process of machine learning articles.
