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.
Adaptive Mixtures of Local Experts
19912.8k citationsRobert A. Jacobs, Michael I. Jordan et al.Neural Computationprofile →
Hierarchical Mixtures of Experts and the EM Algorithm
19941.6k citationsMichael I. Jordan, Robert A. JacobsNeural Computationprofile →
Increased rates of convergence through learning rate adaptation
Countries citing papers authored by Robert A. Jacobs
Since
Specialization
Citations
This map shows the geographic impact of Robert A. Jacobs'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 Robert A. Jacobs with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert A. Jacobs more than expected).
Fields of papers citing papers by Robert A. Jacobs
This network shows the impact of papers produced by Robert A. Jacobs. 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 Robert A. Jacobs. The network helps show where Robert A. Jacobs may publish in the future.
Co-authorship network of co-authors of Robert A. Jacobs
This figure shows the co-authorship network connecting the top 25 collaborators of Robert A. Jacobs.
A scholar is included among the top collaborators of Robert A. Jacobs 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 Robert A. Jacobs. Robert A. Jacobs is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Jacobs, Robert A., et al.. (2019). Efficient Data Compression Leads to Categorical Bias in Perception and Perceptual Memory.. Cognitive Science. 1369–1375.2 indexed citations
Jacobs, Robert A.. (2016). On Forgetting Fukushima. Japan focus. 14(6). 12.2 indexed citations
7.
Jacobs, Robert A., et al.. (2016). A 3D shape inference model matches human visual object similarity judgments better than deep convolutional neural networks.. Cognitive Science.1 indexed citations
Jacobs, Robert A., et al.. (2011). A Nonparametric Bayesian Model of Visual Short-Term Memory. Cognitive Science. 33(33).1 indexed citations
11.
Tarduno, J. A., et al.. (2009). An Active Vision Approach to Understanding and Improving Visual Training in the Geosciences. 2012 GSA Annual Meeting in Charlotte. 2009.1 indexed citations
Jordan, Michael I. & Robert A. Jacobs. (1998). Modular and hierarchical learning systems. MIT Press eBooks. 579–582.26 indexed citations
18.
Jordan, Michael I. & Robert A. Jacobs. (1991). Hierarchies of adaptive experts. Neural Information Processing Systems. 4. 985–992.96 indexed citations
19.
Jacobs, Robert A. & Michael I. Jordan. (1990). A competitive modular connectionist architecture. Neural Information Processing Systems. 3. 767–773.90 indexed citations
20.
Jordan, Michael I. & Robert A. Jacobs. (1989). Learning to Control an Unstable System with Forward Modeling. neural information processing systems. 2. 324–331.67 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.