Lorenz Kuhn

649 total citations · 1 hit paper
4 papers, 203 citations indexed

About

Lorenz Kuhn is a scholar working on Artificial Intelligence, Molecular Biology and Radiological and Ultrasound Technology. According to data from OpenAlex, Lorenz Kuhn has authored 4 papers receiving a total of 203 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 1 paper in Molecular Biology and 1 paper in Radiological and Ultrasound Technology. Recurrent topics in Lorenz Kuhn's work include Topic Modeling (4 papers), Machine Learning in Healthcare (3 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). Lorenz Kuhn is often cited by papers focused on Topic Modeling (4 papers), Machine Learning in Healthcare (3 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). Lorenz Kuhn collaborates with scholars based in Switzerland and United Kingdom. Lorenz Kuhn's co-authors include Yarin Gal, Jannik Kossen, Sebastian Farquhar, Carsten Eickhoff, Chiara Marchiori, Ce Zhang and Nora Hollenstein and has published in prestigious journals such as Nature and Text REtrieval Conference.

In The Last Decade

Lorenz Kuhn

4 papers receiving 194 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.

Detecting hallucinations in large language models using semantic entropy

2024 187 citations Sebastian Farquhar, Jannik Kossen et al. Nature profile →

Peers

Lorenz Kuhn

AI

HI

MB

RNMI

IS

Sebastian Farquhar United Kingdom

Heliodoro Tejeda United States

Viet Dac Lai United States

Andrés Páez Colombia

Nicole Sultanum Canada

Wenxiong Liao China

Thomas Hartvigsen United States

Jan Trienes Germany

Jérémie Clos United Kingdom

Sebastian Farquhar United Kingdom

Lorenz Kuhn

86 ×1.0

AI

51 ×1.0

HI

26 ×0.8

MB

20 ×1.0

RNMI

12 ×0.9

IS

Citations per year, relative to Lorenz Kuhn Lorenz Kuhn (= 1×) peers Sebastian Farquhar

Countries citing papers authored by Lorenz Kuhn

Since Specialization

Citations

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

Fields of papers citing papers by Lorenz Kuhn

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lorenz Kuhn

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

All Works

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4 of 4 papers shown

1.

Farquhar, Sebastian, Jannik Kossen, Lorenz Kuhn, & Yarin Gal. (2024). Detecting hallucinations in large language models using semantic entropy. Nature. 630(8017). 625–630. 187 indexed citations breakdown →

2.

Hollenstein, Nora, et al.. (2018). Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks. 139–148. 9 indexed citations

3.

Kuhn, Lorenz, et al.. (2016). ETH Zurich at TREC Clinical Decision Support 2016.. Text REtrieval Conference. 3 indexed citations

4.

Kuhn, Lorenz & Carsten Eickhoff. (2016). Implicit Negative Feedback in Clinical Information Retrieval. 4 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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