The field of bibliometrics, studying citations and behavior, is critical to
the discussion of reproducibility. Citations are one of the primary incentive
and reward systems for academic work, and so we desire to know if this
incentive rewards reproducible work. Yet to the best of our knowledge, only one
work has attempted to look at this combined space, concluding that
non-reproducible work is more highly cited. We show that answering this
question is more challenging than first proposed, and subtle issues can inhibit
a robust conclusion. To make inferences with more robust behavior, we propose a
hierarchical Bayesian model that incorporates the citation rate over time,
rather than the total number of citations after a fixed amount of time. In
doing so we show that, under current evidence the answer is more likely that
certain fields of study such as Medicine and Machine Learning (ML) do correlate
reproducible works with more citations, but other fields appear to have no
relationship. Further, we find that making code available and thoroughly
referencing prior works appear to also positively correlate with increased
citations. Our code and data can be found at
this https URL .

研究了引用和行为的文献计量学领域，通过提出一种分层贝叶斯模型，从时间上考虑引用率而不是一段时间的总引用数，得出性质不同的学科在可重复性工作之间存在或不存在相关性的结论；发现提供代码并完整引用前人的研究将有助于增加文章引用量。

引述市场是否奖励可重复性的研究工作？

Does the Market of Citations Reward Reproducible Work?

Recent unsupervised representation learning methods have shown to be
effective in a range of vision tasks by learning representations invariant to
data augmentations such as random cropping and color jittering. However, such
invariance could be harmful to downstream tasks if they rely on the
characteristics of the data augmentations, e.g., location- or color-sensitive.
This is not an issue just for unsupervised learning; we found that this occurs
even in supervised learning because it also learns to predict the same label
for all augmented samples of an instance. To avoid such failures and obtain
more generalizable representations, we suggest to optimize an auxiliary
self-supervised loss, coined AugSelf, that learns the difference of
augmentation parameters (e.g., cropping positions, color adjustment
intensities) between two randomly augmented samples. Our intuition is that
AugSelf encourages to preserve augmentation-aware information in learned
representations, which could be beneficial for their transferability.
Furthermore, AugSelf can easily be incorporated into recent state-of-the-art
representation learning methods with a negligible additional training cost.
Extensive experiments demonstrate that our simple idea consistently improves
the transferability of representations learned by supervised and unsupervised
methods in various transfer learning scenarios. The code is available at
this https URL

为了避免生成数据不敏感的特点，同时获取更具普适性的表示，我们提出了一种辅助自监督损失 AugSelf，它学习两个随机样本之间的增强参数差异，并可以轻松地纳入最先进的表征学习方法中，稳定提高了监督和无监督方法的表征迁移性能。