Context: Deep learning has achieved remarkable progress in various domains.
However, like traditional software systems, deep learning systems contain bugs,
which can have severe impacts, as evidenced by crashes involving autonomous
vehicles. Despite substantial advancements in deep learning techniques, little
research has focused on reproducing deep learning bugs, which hinders resolving
them. Existing literature suggests that only 3% of deep learning bugs are
reproducible, underscoring the need for further research.
Objective: This paper examines the reproducibility of deep learning bugs. We
identify edit actions and useful information that could improve deep learning
bug reproducibility.
Method: First, we construct a dataset of 668 deep learning bugs from Stack
Overflow and Defects4ML across 3 frameworks and 22 architectures. Second, we
select 102 bugs using stratified sampling and try to determine their
reproducibility. While reproducing these bugs, we identify edit actions and
useful information necessary for their reproduction. Third, we used the Apriori
algorithm to identify useful information and edit actions required to reproduce
specific bug types. Finally, we conduct a user study with 22 developers to
assess the effectiveness of our findings in real-life settings.
Results: We successfully reproduced 85 bugs and identified ten edit actions
and five useful information categories that can help us reproduce deep learning
bugs. Our findings improved bug reproducibility by 22.92% and reduced
reproduction time by 24.35% based on our user study.
Conclusions: Our research addresses the critical issue of deep learning bug
reproducibility. Practitioners and researchers can leverage our findings to
improve deep learning bug reproducibility.

本研究探讨了深度学习错误的可重现性，通过构建数据集、确定可重现性并识别编辑操作和有用信息，最终成功复现了 85 个深度学习错误并提出了十种编辑操作和五种有用信息类别，有效提高了错误的可重现性。

提升深度学习错误的可复制性：实证研究

Towards Enhancing the Reproducibility of Deep Learning Bugs: An  Empirical Study

Attention in neural machine translation provides the possibility to encode
relevant parts of the source sentence at each translation step. As a result,
attention is considered to be an alignment model as well. However, there is no
work that specifically studies attention and provides analysis of what is being
learned by attention models. Thus, the question still remains that how
attention is similar or different from the traditional alignment. In this
paper, we provide detailed analysis of attention and compare it to traditional
alignment. We answer the question of whether attention is only capable of
modelling translational equivalent or it captures more information. We show
that attention is different from alignment in some cases and is capturing
useful information other than alignments.

本研究提供详细分析和比较注意力和传统对齐，在一些情况下表明注意力不同于对齐且能捕捉除对齐以外的有用信息。