Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
This map shows the geographic impact of Aaron Klein's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Aaron Klein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aaron Klein more than expected).
This network shows the impact of papers produced by Aaron Klein. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Aaron Klein. The network helps show where Aaron Klein may publish in the future.
Co-authorship network of co-authors of Aaron Klein
This figure shows the co-authorship network connecting the top 25 collaborators of Aaron Klein.
A scholar is included among the top collaborators of Aaron Klein based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Aaron Klein. Aaron Klein is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Franceschi, Luca, Michele Donini, Valerio Perrone, et al.. (2025). Hyperparameter Optimization in Machine Learning. Florence Research (University of Florence). 18(6). 1054–1201.1 indexed citations
2.
Klein, Aaron, et al.. (2021). Dynamic pruning of a neural network via gradient signal-to-noise ratio.2 indexed citations
Ilg, Eddy, Özgün Çiçek, Aaron Klein, et al.. (2018). Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks. arXiv (Cornell University).3 indexed citations
7.
Falkner, Stefan, Aaron Klein, & Frank Hutter. (2018). Practical Hyperparameter Optimization for Deep Learning. International Conference on Learning Representations.12 indexed citations
8.
Klein, Aaron, Eric Christiansen, Kevin D. Murphy, & Frank Hutter. (2018). Towards Reproducible Neural Architecture and Hyperparameter Search.7 indexed citations
Klein, Aaron, Stefan Falkner, Jost Tobias Springenberg, & Frank Hutter. (2017). Learning Curve Prediction with Bayesian Neural Networks. International Conference on Learning Representations.67 indexed citations
Klein, Aaron. (1971). On Categories of Quotients. Proceedings of the American Mathematical Society. 30(2). 205–205.1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.