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.
Support vector clustering
2002911 citationsAsa Ben‐Hur, D. Horn et al.profile →
Finite-Energy Sum Rules and Their Application toπNCharge Exchange
This map shows the geographic impact of D. Horn'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 D. Horn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites D. Horn more than expected).
This network shows the impact of papers produced by D. Horn. 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 D. Horn. The network helps show where D. Horn may publish in the future.
Co-authorship network of co-authors of D. Horn
This figure shows the co-authorship network connecting the top 25 collaborators of D. Horn.
A scholar is included among the top collaborators of D. Horn 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 D. Horn. D. Horn is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Solan, Zach, D. Horn, Eytan Ruppin, & Shimon Edelman. (2003). Unsupervised Context Sensitive Language Acquisition from a Large Corpus. Neural Information Processing Systems. 16. 961–968.16 indexed citations
10.
Horn, D., et al.. (2003). The Doubly Balanced Network of Spiking Neurons: A Memory Model with High Capacity. Neural Information Processing Systems. 16. 1247–1254.3 indexed citations
11.
Solan, Zach, D. Horn, Eytan Ruppin, & Shimon Edelman. (2003). Unsupervised Efficient Learning and Representation of Language Structure. eScholarship (California Digital Library). 25(25).9 indexed citations
12.
Solan, Zach, Eytan Ruppin, D. Horn, & Shimon Edelman. (2002). Automatic Acquisition and Efficient Representation of Syntactic Structures. Neural Information Processing Systems. 15. 107–114.17 indexed citations
Ben‐Hur, Asa, D. Horn, Hava T. Siegelmann, & Vladimir Vapnik. (2000). A Support Vector Method for Clustering. ScholarWorks@UMassAmherst (University of Massachusetts Amherst). 13. 367–373.31 indexed citations
15.
Horn, D., Nir Levy, Isaac Meilijson, & Eytan Ruppin. (1999). Distributed Synchrony of Spiking Neurons in a Hebbian Cell Assembly. Neural Information Processing Systems. 12. 129–135.17 indexed citations
16.
Dror, Gideon, H. Abramowicz, & D. Horn. (1998). Vertex Identification in High Energy Physics Experiments. Neural Information Processing Systems. 11. 868–874.1 indexed citations
17.
Abramowicz, H., et al.. (1996). An Orientation Selective Neural Network for Pattern Identification in Particle Detectors. Neural Information Processing Systems. 9. 925–931.3 indexed citations
18.
Ruppin, Eytan, James A. Reggia, & D. Horn. (1994). A Neural Model of Delusions and Hallucinations in Schizophrenia. Neural Information Processing Systems. 7. 149–156.4 indexed citations
19.
Ginzburg, Iris & D. Horn. (1993). Combined Neural Networks for Time Series Analysis. Neural Information Processing Systems. 6. 224–231.52 indexed citations
20.
Horn, D. & Marius Usher. (1991). Oscillatory Model of Short Term Memory. Neural Information Processing Systems. 4. 125–132.18 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.