Countries citing papers authored by Jonathan Gordon
Since
Specialization
Citations
This map shows the geographic impact of Jonathan Gordon'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 Jonathan Gordon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Gordon more than expected).
This network shows the impact of papers produced by Jonathan Gordon. 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 Jonathan Gordon. The network helps show where Jonathan Gordon may publish in the future.
Co-authorship network of co-authors of Jonathan Gordon
This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan Gordon.
A scholar is included among the top collaborators of Jonathan Gordon 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 Jonathan Gordon. Jonathan Gordon is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Foong, Andrew Y. K., et al.. (2020). Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes. Cambridge University Engineering Department Publications Database. 33. 8284–8295.1 indexed citations
4.
Gordon, Jonathan, David López-Paz, Marco Baroni, & Diane Bouchacourt. (2020). Permutation Equivariant Models for Compositional Generalization in Language. International Conference on Learning Representations.28 indexed citations
Pinsler, Robert, Jonathan Gordon, Eric Nalisnick, & José Miguel Hernández-Lobato. (2019). Bayesian Batch Active Learning as Sparse Subset Approximation. UvA-DARE (University of Amsterdam). 32. 6356–6367.12 indexed citations
Gordon, Jonathan. (2014). Inferential commonsense knowledge from text. UR Research (University of Rochester).2 indexed citations
13.
Gordon, Jonathan & Lenhart K. Schubert. (2012). Using Textual Patterns to Learn Expected Event Frequencies. UR Research (University of Rochester). 122–127.3 indexed citations
14.
Schubert, Lenhart K., et al.. (2011). Towards Adequate Knowledge and Natural Inference. UR Research (University of Rochester). 288–296.1 indexed citations
15.
Gordon, Jonathan & Lenhart K. Schubert. (2011). Discovering Commonsense Entailment Rules Implicit in Sentences. UR Research (University of Rochester). 59–63.7 indexed citations
16.
Gordon, Jonathan, Benjamin Van Durme, & Lenhart K. Schubert. (2010). Evaluation of Commonsense Knowledge with Mechanical Turk. UR Research (University of Rochester). 159–162.12 indexed citations
17.
Gordon, Jonathan & Lenhart K. Schubert. (2010). Quantificational Sharpening of Commonsense Knowledge. National Conference on Artificial Intelligence.11 indexed citations
18.
Gordon, Jonathan, Benjamin Van Durme, & Lenhart K. Schubert. (2010). Learning from the web: extracting general world knowledge from noisy text. UR Research (University of Rochester). 10–15.17 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.