arXiv：cs.LG· Honglin Bao, Beichen Lu, Kai Li·· 4 小时前AI 评分34
动态科学中的坚持悖论：来自深度学习革命的证据
Persistence Paradox in Dynamic Science: Evidence from the Deep Learning Revolution
AI 导读
一项基于5,000余名顶级机器学习会议科学家20年职业轨迹的研究发现，在AlexNet引发的深度学习革命中，此前的成功者或隶属成熟团队的研究者转向更慢。坚持与产出正相关，但在2012年后与科学影响力负相关。多数研究者聚集在中等坚持区间，揭示产出与影响力之间的权衡，以及阻碍大幅转向的机构摩擦。
正文
Abstract:Persistence is often regarded as a virtue in science. In this paper, however, we challenge this conventional view by highlighting its contextual nature, particularly how persistence can become a liability during paradigm shifts. We focus on the deep learning revolution catalyzed by AlexNet in 2012. Analyzing the 20-year career trajectories of more than 5,000 scientists active in top machine learning venues during the preceding decade, we examine how their research focus and output evolved. We first uncover a dynamic period in which leading venues increasingly prioritized cutting-edge deep learning developments, displacing traditional statistical learning methods. Scientists responded to these changes in markedly different ways: those who were previously successful or affiliated with established teams adapted more slowly. Such persistence is positively associated with productivity but, after 2012, negatively associated with scientific impact. Most researchers, and the largest share of the field's output, cluster in a band of moderate persistence, pointing to a trade-off between output and impact, as well as to institutional frictions that make larger departures costly. These conclusions are robust to alternative identification strategies and to competing explanations such as topic popularity premiums and survivorship bias. Taken together, our macro- and micro-level findings suggest that, in this case, a paradigm shift creates an opportunity structure by devaluing the very expertise that conferred incumbents' advantage in the first place.
| Subjects: | Digital Libraries (cs.DL); Computers and Society (cs.CY); Machine Learning (cs.LG) |
| Cite as: | arXiv:2506.22729 [cs.DL] |
| (or arXiv:2506.22729v3 [cs.DL] for this version) | |
| https://doi.org/10.48550/arXiv.2506.22729 arXiv-issued DOI via DataCite |
Submission history
From: Honglin Bao [view email]
[v1]
Sat, 28 Jun 2025 02:21:19 UTC (2,890 KB)
[v2]
Tue, 1 Jul 2025 16:14:58 UTC (2,657 KB)
[v3]
Tue, 6 Oct 2026 21:31:01 UTC (3,419 KB)
来源：arXiv：cs.LG · arxiv.org