论文标题
机器学习中的差异复制
Differential Replication in Machine Learning
论文作者
论文摘要
当部署在野外时，机器学习模型通常面临不断变化的数据和要求，要么是因为生成分布的变化，要么是因为外部约束会改变模型运行的环境。为了在这样的生态系统中生存，机器学习模型需要随着时间的流逝而发展。已经从不同的角度研究了模型适应性的想法。在本文中，我们提出了一种基于重用已经部署的机器学习模型获得的知识并利用其培训子孙后代的知识的解决方案。这是机器学习模型的差异复制背后的想法。
When deployed in the wild, machine learning models are usually confronted with data and requirements that constantly vary, either because of changes in the generating distribution or because external constraints change the environment where the model operates. To survive in such an ecosystem, machine learning models need to adapt to new conditions by evolving over time. The idea of model adaptability has been studied from different perspectives. In this paper, we propose a solution based on reusing the knowledge acquired by the already deployed machine learning models and leveraging it to train future generations. This is the idea behind differential replication of machine learning models.
