Martin Binder

1.4k total citations · 1 hit paper
10 papers, 693 citations indexed

About

Martin Binder is a scholar working on Artificial Intelligence, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Martin Binder has authored 10 papers receiving a total of 693 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 3 papers in Molecular Biology and 3 papers in Computational Theory and Mathematics. Recurrent topics in Martin Binder's work include Machine Learning and Data Classification (5 papers), Machine Learning in Bioinformatics (3 papers) and Advanced Multi-Objective Optimization Algorithms (3 papers). Martin Binder is often cited by papers focused on Machine Learning and Data Classification (5 papers), Machine Learning in Bioinformatics (3 papers) and Advanced Multi-Objective Optimization Algorithms (3 papers). Martin Binder collaborates with scholars based in Germany, United States and United Kingdom. Martin Binder's co-authors include Bernd Bischl, Michel Lang, Stefan Coors, Jakob Richter, Tobias Pielok, Janek Thomas, Difan Deng, Marc Becker, Marius Lindauer and Theresa Ullmann and has published in prestigious journals such as IEEE Transactions on Evolutionary Computation, Journal of Machine Learning Research and Communications Biology.

In The Last Decade

Martin Binder

9 papers receiving 673 citations

Hit Papers

align trajectories

What are hit papers?

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.

Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges

2023 398 citations Bernd Bischl, Martin Binder et al. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery profile →

Peers

Martin Binder

AI

MB

EE

EEE

ECOLO

Stefan Coors Germany

Waldemar Ratajczak Poland

Ekaba Bisong United States

Steven J. Rigatti United States

Alfonso Iodice D’Enza Italy

Bastian Bohn Germany

Khaled Fawagreh Saudi Arabia

Dimane Mpoeleng Botswana

Su-Wen Huang Taiwan

Xiao Su United States

Stefan Coors Germany

Martin Binder

200 ×1.1

AI

63 ×0.9

MB

58 ×1.0

EE

56 ×1.0

EEE

41 ×1.0

ECOLO

Citations per year, relative to Martin Binder Martin Binder (= 1×) peers Stefan Coors

Countries citing papers authored by Martin Binder

Since Specialization

Citations

This map shows the geographic impact of Martin Binder'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 Martin Binder with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Martin Binder more than expected).

Fields of papers citing papers by Martin Binder

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Martin Binder. 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 Martin Binder. The network helps show where Martin Binder may publish in the future.

Co-authorship network of co-authors of Martin Binder

This figure shows the co-authorship network connecting the top 25 collaborators of Martin Binder. A scholar is included among the top collaborators of Martin Binder 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 Martin Binder. Martin Binder is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Franzosa, Eric A., Curtis Huttenhower, Mina Rezaei, et al.. (2024). Optimized model architectures for deep learning on genomic data. Communications Biology. 7(1). 516–516. 1 indexed citations

2.

Pielok, Tobias, Florian Pfisterer, Stefan Coors, et al.. (2023). Multi-Objective Hyperparameter Optimization in Machine Learning—An Overview. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 3(4). 1–50. 36 indexed citations

3.

Bischl, Bernd, Martin Binder, Michel Lang, et al.. (2023). Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 13(2). 398 indexed citations breakdown →

4.

Binder, Martin, et al.. (2023). A self-supervised deep learning method for data-efficient training in genomics. Communications Biology. 6(1). 928–928. 9 indexed citations

5.

Münch, Philipp C., et al.. (2023). Neural Architecture Search for Genomic Sequence Data. Open access LMU (Ludwid Maxmilian's Universitat Munchen). 1–10.

6.

Binder, Martin, Florian Pfisterer, Marc Becker, et al.. (2022). Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers. IEEE Transactions on Evolutionary Computation. 26(6). 1336–1350. 2 indexed citations

7.

Binder, Martin, et al.. (2021). mlr3pipelines - Flexible Machine Learning Pipelines in R. Journal of Machine Learning Research. 22(184). 1–7. 9 indexed citations

8.

Binder, Martin, et al.. (2021). Preprocessing Operators and Pipelines for 'mlr3' [R package mlr3pipelines version 0.3.6-1]. 1 indexed citations

9.

Lang, Michel, Martin Binder, Jakob Richter, et al.. (2019). mlr3: A modern object-oriented machine learning framework in R. The Journal of Open Source Software. 4(44). 1903–1903. 235 indexed citations

10.

Ganster, Harald, R. Röhrer, Lucas Paletta, et al.. (1998). Comparison of neural networks and statistical methods in melanoma classification. 97–105. 2 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.

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