论文标题
机器学习的分析水平
Levels of Analysis for Machine Learning
论文作者
论文摘要
机器学习目前参与了有史以来最激烈的辩论。这样的辩论似乎经常在圈子中四处走动，没有达成任何结论或解决方案。鉴于机器学习中的研究人员以截然不同的参考框架进行了这些讨论，这可能使他们挑战他们，使他们对观点保持一致并找到共同点，这也许并不奇怪。作为解决这一难题的一种补救措施，我们主张采用一个共同的概念框架，该概念框架可用于理解，分析和讨论研究。我们提出了一个在认知科学和神经科学中很受欢迎的框架，并且我们认为这在机器学习方面也有很大的效用：MARR的分析水平。通过一系列案例研究，我们演示了这些水平如何促进对机器学习的几种方法的理解和解剖。通过在自己的工作中采用分析水平，我们认为，研究人员可以更好地参与推动我们领域进步所必需的辩论。
Machine learning is currently involved in some of the most vigorous debates it has ever seen. Such debates often seem to go around in circles, reaching no conclusion or resolution. This is perhaps unsurprising given that researchers in machine learning come to these discussions with very different frames of reference, making it challenging for them to align perspectives and find common ground. As a remedy for this dilemma, we advocate for the adoption of a common conceptual framework which can be used to understand, analyze, and discuss research. We present one such framework which is popular in cognitive science and neuroscience and which we believe has great utility in machine learning as well: Marr's levels of analysis. Through a series of case studies, we demonstrate how the levels facilitate an understanding and dissection of several methods from machine learning. By adopting the levels of analysis in one's own work, we argue that researchers can be better equipped to engage in the debates necessary to drive forward progress in our field.
