机器学习的层次分析(CS CY)---Elva

机器学习目前正在进行一些前所未见的激烈辩论。这样的辩论似乎常常是绕圈子，最后也没有得出结论或解决办法。这也许并不奇怪，因为机器学习领域的研究人员以非常不同的参照系参加这些讨论，这使得他们很难调整观点并找到共同点。为了解决这一困境，我们主张采用一个共同的概念框架，可以用来理解、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流行的框架：Marr的层次分析，相信它在机器学习中也有很大的用处。通过一系列的案例研究，我们展示了这些层次如何促进对机器学习中几种方法的理解和剖析。我们认为，通过在自己的工作中采用这些分析层次，研究人员可以更好地参与必要的辩论，推动机器学习领域的进展。

原文题目：Levels of Analysis for Machine Learning

原文：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.

原文作者：Jessica Hamrick, Shakir Mohamed

原文地址：https://arxiv.org/abs/2004.05107