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.
Toward memory-based reasoning
1986811 citationsCraig Stanfill, David L. Waltzprofile →
Countries citing papers authored by David L. Waltz
Since
Specialization
Citations
This map shows the geographic impact of David L. Waltz'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 David L. Waltz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David L. Waltz more than expected).
This network shows the impact of papers produced by David L. Waltz. 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 David L. Waltz. The network helps show where David L. Waltz may publish in the future.
Co-authorship network of co-authors of David L. Waltz
This figure shows the co-authorship network connecting the top 25 collaborators of David L. Waltz.
A scholar is included among the top collaborators of David L. Waltz 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 David L. Waltz. David L. Waltz is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Dutta, Haimonti, et al.. (2011). Learning parameters of the K-means algorithm from subjective human annotation. The Florida AI Research Society. 465–470.2 indexed citations
Kasif, Simon, et al.. (1996). Local induction of decision trees: towards interactive data mining. Knowledge Discovery and Data Mining. 14–19.8 indexed citations
7.
Stanfill, Craig & David L. Waltz. (1992). Statistical methods, artificial intelligence, and information retrieval. 215–225.10 indexed citations
8.
Waltz, David L.. (1989). The prospects for building truly intelligent machines. MIT Press eBooks. 191–212.16 indexed citations
9.
Waltz, David L. & Craig Stanfill. (1988). Artificial Intelligence Related Research on the Connection Machine.. Future Generation Computer Systems. 1010–1024.2 indexed citations
10.
Waltz, David L. & Jerome A. Feldman. (1988). Connectionist models and their implications: readings from cognitive science. Ablex Publishing Corp. eBooks.59 indexed citations
11.
Waltz, David L. & Jerome A. Feldman. (1988). Connectionist models and their implications. Ablex Publishing Corp. eBooks. 1–11.24 indexed citations
Waltz, David L. & Jordan Pollack. (1984). Phenomenologically plausible parsing. National Conference on Artificial Intelligence. 335–339.6 indexed citations
Waltz, David L.. (1980). Generating and Understanding Scene Descriptions.. Defense Technical Information Center (DTIC).8 indexed citations
17.
Waltz, David L. & Lois Boggess. (1979). Visual, analog representations for natural languages understanding. International Joint Conference on Artificial Intelligence. 926–934.21 indexed citations
18.
Waltz, David L.. (1978). Theoretical Issues in Natural Language Processing-2.63 indexed citations
19.
Waltz, David L.. (1978). Proceedings of the 1978 workshop on Theoretical issues in natural language processing.1 indexed citations
20.
Waltz, David L. & Bradley A. Goodman. (1977). Writing a natural language data base system. International Joint Conference on Artificial Intelligence. 144–150.26 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.