Countries citing papers authored by Helge Langseth
Since
Specialization
Citations
This map shows the geographic impact of Helge Langseth'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 Helge Langseth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Helge Langseth more than expected).
This network shows the impact of papers produced by Helge Langseth. 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 Helge Langseth. The network helps show where Helge Langseth may publish in the future.
Co-authorship network of co-authors of Helge Langseth
This figure shows the co-authorship network connecting the top 25 collaborators of Helge Langseth.
A scholar is included among the top collaborators of Helge Langseth 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 Helge Langseth. Helge Langseth is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Langseth, Helge, et al.. (2021). Probabilistic Models with Deep Neural Networks. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas).8 indexed citations
Aamodt, Agnar, et al.. (2017). Data driven case base construction for prediction of success of marine operations. BIBSYS Brage (BIBSYS (Norway)). 104–113.1 indexed citations
9.
Masegosa, Andrés R., Ana María Martínez, Antonio Salmerón, et al.. (2017). MAP inference in dynamic hybrid Bayesian networks. Progress in Artificial Intelligence. 6(2). 133–144.6 indexed citations
Langseth, Helge, Thomas D. Nielsen, Rafael Rumí, & Antonio Salmerón. (2011). Mixtures of truncated basis functions. International Journal of Approximate Reasoning. 53(2). 212–227.48 indexed citations
13.
Langseth, Helge & Thomas D. Nielsen. (2011). A latent model for collaborative filtering. International Journal of Approximate Reasoning. 53(4). 447–466.28 indexed citations
14.
Martínez, Ana María, et al.. (2010). Towards a more expressive model for dynamic classification. VBN Forskningsportal (Aalborg Universitet). 563–564.3 indexed citations
15.
Langseth, Helge, Thomas D. Nielsen, Rafael Rumí, & Antonio Salmerón. (2008). Parameter Estimation in Mixtures of Truncated Exponentials. VBN Forskningsportal (Aalborg Universitet). 169–176.2 indexed citations
Langseth, Helge & Thomas D. Nielsen. (2003). Fusion of domain knowledge with data for structural learning in object oriented domains. Journal of Machine Learning Research. 4(3). 339–368.31 indexed citations
18.
Langseth, Helge, et al.. (2001). Structural Learning in Object Oriented Domains. VBN Forskningsportal (Aalborg Universitet). 340–344.11 indexed citations
19.
Langseth, Helge & Finn V. Jensen. (2001). Heuristics for Two Extensions of Basic Troubleshooting. 80–89.17 indexed citations
20.
Langseth, Helge, et al.. (1999). Learning Retrieval Knowledge from Data.5 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.