最新技术:标杆文化的时间秩序。

Alexander Campolo
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引用次数: 0

摘要

这篇评论将机器学习的基准和评估文化的认知价值置于更大的时间结构中。除了有效性问题之外,模型比较是否在统计上有效,或者基准是否充分代表有意义的任务或能力,它还询问基准如何产生特定的时间值和期望。它阐明了两个假设作为回应:第一个,被称为正常化研究,试图描述基准如何同时在研究中发挥纪律和激励作用,从而最大限度地减少冲突。第二种,被称为外推,认为基准测试的增量,渐进式节奏与其说是面向未来,不如说是面向当前的最先进技术(SOTA)。总之,这些假设提供了对机器学习中基准测试和评估的现实性的诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
State-of-the-Art: The Temporal Order of Benchmarking Culture.

This commentary situates the epistemic values of machine learning's culture of benchmarking and evaluation within larger temporal structures. Beyond questions of validity, whether model comparisons are statistically valid or whether benchmarks adequately represent meaningful tasks or capabilities, it asks how benchmarks produce certain temporal values and expectations. It articulates two hypotheses in response: the first, termed normalizing research, seeks to characterize how benchmarking simultaneously serves a disciplining and motivating function in research, with the effect of minimizing conflict. The second, termed extrapolation, argues that the incremental, progressive rhythm of benchmarking is oriented less towards the future than towards a present state-of-the-art (SOTA). Together, these hypotheses inform a diagnosis of the presentist temporality of benchmarking and evaluation in machine learning.

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