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.
Fast Learning in Networks of Locally-Tuned Processing Units
19893.0k citationsJohn Moody, Christian J. DarkenNeural Computationprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Christian J. Darken
Since
Specialization
Citations
This map shows the geographic impact of Christian J. Darken'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 Christian J. Darken with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christian J. Darken more than expected).
Fields of papers citing papers by Christian J. Darken
This network shows the impact of papers produced by Christian J. Darken. 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 Christian J. Darken. The network helps show where Christian J. Darken may publish in the future.
Co-authorship network of co-authors of Christian J. Darken
This figure shows the co-authorship network connecting the top 25 collaborators of Christian J. Darken.
A scholar is included among the top collaborators of Christian J. Darken 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 Christian J. Darken. Christian J. Darken is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Darken, Christian J., et al.. (2012). Faster conceptual blending predictors on relational time series. Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School). 188–195.1 indexed citations
Darken, Christian J., et al.. (2008). Blended Inverse Kinematics: Delta3D System Utilization. Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School).1 indexed citations
8.
Darken, Christian J., et al.. (2008). A Reference Model of Soldier Attention and Behavior. Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School).6 indexed citations
Darken, Christian J.. (2007). Level Annotation and Test by Autonomous Exploration. Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School).9 indexed citations
11.
Darken, Christian J., et al.. (2006). A Mental Simulation-Based Decision-Making Architecture Applied to Ground Combat.3 indexed citations
Darken, Christian J.. (1998). Stochastic approximation and neural network learning. MIT Press eBooks. 941–945.2 indexed citations
16.
Scheffer, Tobias, Russell Greiner, & Christian J. Darken. (1997). Why Experimentation can be better than Perfect Guidance. International Conference on Machine Learning. 331–339.5 indexed citations
17.
Petsche, Thomas, et al.. (1995). A Neural Network Autoassociator for Induction Motor Failure Prediction. Neural Information Processing Systems. 8. 924–930.32 indexed citations
18.
Darken, Christian J. & John Moody. (1991). Towards Faster Stochastic Gradient Search. Neural Information Processing Systems. 4. 1009–1016.64 indexed citations
19.
Darken, Christian J. & John Moody. (1990). Note on Learning Rate Schedules for Stochastic Optimization. Neural Information Processing Systems. 3. 832–838.93 indexed citations
20.
Moody, John & Christian J. Darken. (1989). Fast Learning in Networks of Locally-Tuned Processing Units. Neural Computation. 1(2). 281–294.2980 indexed citations breakdown →
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.