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.
Countries citing papers authored by Michael C. Mozer
Since
Specialization
Citations
This map shows the geographic impact of Michael C. Mozer'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 Michael C. Mozer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael C. Mozer more than expected).
Fields of papers citing papers by Michael C. Mozer
This network shows the impact of papers produced by Michael C. Mozer. 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 Michael C. Mozer. The network helps show where Michael C. Mozer may publish in the future.
Co-authorship network of co-authors of Michael C. Mozer
This figure shows the co-authorship network connecting the top 25 collaborators of Michael C. Mozer.
A scholar is included among the top collaborators of Michael C. Mozer 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 Michael C. Mozer. Michael C. Mozer is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lamb, Alex, Jonathan Binas, Anirudh Goyal, et al.. (2019). State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. International Conference on Machine Learning. 3622–3631.1 indexed citations
3.
Mozer, Michael C., et al.. (2018). Can Textbook Annotations Serve as an Early Predictor of Student Learning. Educational Data Mining.5 indexed citations
4.
Beckage, Nicole, Michael C. Mozer, & Eliana Colunga. (2015). Predicting a Child's Trajectory of Lexical Acquisition.. Cognitive Science.1 indexed citations
5.
Khajah, Mohammad, Yun Huang, José P. González-Brenes, Michael C. Mozer, & Peter Brusilovsky. (2014). Integrating Knowledge Tracing and Item Response Theory: A Tale of Two Frameworks. D-Scholarship@Pitt (University of Pittsburgh).36 indexed citations
6.
Lindsey, Robert, Michael C. Mozer, William J. Huggins, & Harold Pashler. (2013). Optimizing Instructional Policies. Neural Information Processing Systems. 26. 2778–2786.19 indexed citations
7.
Wager, Tor D., et al.. (2011). Past Experience Influences Judgment of Pain: Prediction of Sequential Dependencies.. Cognitive Science. 33(33).3 indexed citations
8.
Bohté, Sander M. & Michael C. Mozer. (2004). Reducing Spike Train Variability: A Computational Theory Of Spike-Timing Dependent Plasticity. Data Archiving and Networked Services (DANS). 17. 201–208.7 indexed citations
9.
Yan, Lian, Robert H. Dodier, Michael C. Mozer, & Richard H. Wolniewicz. (2003). Optimizing classifier performance via an approximation to the Wilcoxon-Mann-Whitney statistic. International Conference on Machine Learning. 848–855.131 indexed citations
10.
Lee, Soo-Young & Michael C. Mozer. (1999). Robust Recognition of Noisy and Superimposed Patterns via Selective Attention. Neural Information Processing Systems. 12. 31–37.3 indexed citations
11.
Zemel, Richard S. & Michael C. Mozer. (1999). A Generative Model for Attractor Dynamics. Neural Information Processing Systems. 12. 80–88.1 indexed citations
12.
Mozer, Michael C., et al.. (1999). Churn Reduction in the Wireless Industry. Neural Information Processing Systems. 12. 935–941.28 indexed citations
13.
Mozer, Michael C.. (1998). A Principle for Unsupervised Hierarchical Decomposition of Visual Scenes. Neural Information Processing Systems. 11. 52–58.2 indexed citations
14.
Bachrach, Jonathan & Michael C. Mozer. (1995). Connectionist modeling and control of finite state systems given partial state information. 351–387.2 indexed citations
15.
Mathis, Donald W. & Michael C. Mozer. (1994). On the Computational Utility of Consciousness. Neural Information Processing Systems. 7. 11–18.15 indexed citations
16.
Mozer, Michael C., et al.. (1993). Dynamic Conflict Resolution in a Connectionist Rule-Based System.. International Joint Conference on Artificial Intelligence. 1366–1373.3 indexed citations
17.
Mozer, Michael C., et al.. (1993). A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction. Neural Information Processing Systems. 6. 19–26.30 indexed citations
18.
Mozer, Michael C., et al.. (1991). The Connectionist Scientist Game: Rule Extraction and Refinement in a Neural Network. eScholarship (California Digital Library).11 indexed citations
19.
Mozer, Michael C.. (1990). Discovering Faithful 'Wickelfeature' Representations in a Connectionist Network. Defense Technical Information Center (DTIC).1 indexed citations
20.
Mozer, Michael C. & Paul Smolensky. (1988). Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment. Neural Information Processing Systems. 1. 107–115.322 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.