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.
Learning in the Presence of Concept Drift and Hidden Contexts
Countries citing papers authored by Gerhard Widmer
Since
Specialization
Citations
This map shows the geographic impact of Gerhard Widmer'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 Gerhard Widmer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gerhard Widmer more than expected).
This network shows the impact of papers produced by Gerhard Widmer. 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 Gerhard Widmer. The network helps show where Gerhard Widmer may publish in the future.
Co-authorship network of co-authors of Gerhard Widmer
This figure shows the co-authorship network connecting the top 25 collaborators of Gerhard Widmer.
A scholar is included among the top collaborators of Gerhard Widmer 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 Gerhard Widmer. Gerhard Widmer is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Eghbal-zadeh, Hamid, et al.. (2021). Learning to Infer Unseen Contexts in Causal Contextual Reinforcement Learning. International Conference on Learning Representations.2 indexed citations
8.
Wu, Chih-Wei, Christian Dittmar, Richard Vogl, et al.. (2018). A Review of Automatic Drum Transcription. IEEE/ACM Transactions on Audio Speech and Language Processing. 26(9). 1457–1483.26 indexed citations
Schnitzer, Dominik, Arthur Flexer, Markus Schedl, & Gerhard Widmer. (2012). Local and global scaling reduce hubs in space. Journal of Machine Learning Research. 13(1). 2871–2902.51 indexed citations
11.
Widmer, Gerhard, et al.. (2005). Exploring Similarities in Music Performances with an Evolutionary Algorithm.. The Florida AI Research Society. 80–85.3 indexed citations
12.
Widmer, Gerhard, et al.. (2004). Automatic recognition of famous artists by machine. European Conference on Artificial Intelligence. 1109–1110.7 indexed citations
13.
Stamatatos, Efstathios & Gerhard Widmer. (2002). Music performer recognition using an ensemble of simple classifiers. European Conference on Artificial Intelligence. 335–339.19 indexed citations
14.
Dixon, Simon, Werner Goebl, & Gerhard Widmer. (2002). The Performance Worm: Real Time Visualisation of Expression based on Langner's Tempo-Loudness Animation. The Journal of the Abraham Lincoln Association. 2002.22 indexed citations
15.
Widmer, Gerhard. (2001). Inductive Learning of General and Robust Local Expression Principles. The Journal of the Abraham Lincoln Association. 2001.5 indexed citations
16.
Widmer, Gerhard. (2000). Large-scale Induction of Expressive Performance Rules: First Quantitative Results. The Journal of the Abraham Lincoln Association. 2000.15 indexed citations
17.
Widmer, Gerhard. (1996). Recognition and Exploitation of Contextual CLues via Incremental Meta-Learning.. International Conference on Machine Learning. 525–533.12 indexed citations
18.
Widmer, Gerhard. (1994). Combining robustness and flexibility in learning drifting concepts. European Conference on Artificial Intelligence. 468–472.17 indexed citations
19.
Widmer, Gerhard. (1993). Combining Knowledge-Based and Instance-Based Learning to Exploit Qualitative Knowledge.. Informatica (slovenia). 17.11 indexed citations
20.
Widmer, Gerhard. (1992). The importance of basic musical knowledge for effective learning. MIT Press eBooks. 490–507.3 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.