Machine learning (ML) has emerged as a prominent field of research in
computer science and other related fields, thereby driving advancements in
other domains of interest. As the field continues to evolve, it is crucial to
understand the landscape of highly cited publications to identify key trends,
influential authors, and significant contributions made thus far. In this
paper, we present a comprehensive bibliometric analysis of highly cited ML
publications. We collected a dataset consisting of the top-cited papers from
reputable ML conferences and journals, covering a period of several years from
1959 to 2022. We employed various bibliometric techniques to analyze the data,
including citation analysis, co-authorship analysis, keyword analysis, and
publication trends. Our findings reveal the most influential papers, highly
cited authors, and collaborative networks within the machine learning
community. We identify popular research themes and uncover emerging topics that
have recently gained significant attention. Furthermore, we examine the
geographical distribution of highly cited publications, highlighting the
dominance of certain countries in ML research. By shedding light on the
landscape of highly cited ML publications, our study provides valuable insights
for researchers, policymakers, and practitioners seeking to understand the key
developments and trends in this rapidly evolving field.

通过对高被引机器学习论文进行综合评析，我们揭示了机器学习领域内最有影响力的论文、高被引作者以及合作网络。我们还发现了热门研究主题和最新受到关注的新兴话题，并对高被引论文的地理分布进行了分析，突显了某些国家在机器学习研究中的主导地位。通过为研究人员、政策制定者和实践者提供关于这一快速发展领域的主要进展和趋势的有价值的见解，我们的研究为它们提供了有价值的洞察。

六十年来具有重要影响力和高引用量的机器学习研究综合研究

A Comprehensive Study of Groundbreaking Machine Learning Research:  Analyzing Highly Cited and Impactful Publications across Six Decades

Deep machine learning models are increasingly deployedin the wild for
providing services to users. Adversaries maysteal the knowledge of these
valuable models by trainingsubstitute models according to the inference results
of thetargeted deployed models. Recent data-free model stealingmethods are
shown effective to extract the knowledge of thetarget model without using real
query examples, but they as-sume rich inference information, e.g., class
probabilities andlogits. However, they are all based on competing
generator-substitute networks and hence encounter training this http URL this
paper we propose a data-free model stealing frame-work,MEGA, which is based on
collaborative generator-substitute networks and only requires the target model
toprovide label prediction for synthetic query examples. Thecore of our method
is a model stealing optimization con-sisting of two collaborative models (i)
the substitute modelwhich imitates the target model through the synthetic
queryexamples and their inferred labels and (ii) the generatorwhich synthesizes
images such that the confidence of thesubstitute model over each query example
is maximized. Wepropose a novel coordinate descent training procedure
andanalyze its convergence. We also empirically evaluate thetrained substitute
model on three datasets and its applicationon black-box adversarial attacks.
Our results show that theaccuracy of our trained substitute model and the
adversarialattack success rate over it can be up to 33% and 40% higherthan
state-of-the-art data-free black-box attacks.

本文提出了一种基于协作生成替代模型网络的无数据模型窃取框架 MEGA，该框架通过合成查询示例和预测标签来模拟目标模型，并且通过最大化替代模型的置信度来生成图像，实验表明，该框架所训练的替代模型和黑盒敌对攻击的成功率均比现有的数据无关的攻击方法高。