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.
A Global Geometric Framework for Nonlinear Dimensionality Reduction
20008.8k citationsJoshua B. Tenenbaum et al.profile →
Human-level concept learning through probabilistic program induction
20151.4k citationsJoshua B. Tenenbaum et al.profile →
How to Grow a Mind: Statistics, Structure, and Abstraction
20111.0k citationsJoshua B. Tenenbaum, Charles Kemp et al.profile →
The Large‐Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth
2005850 citationsJoshua B. Tenenbaum et al.Cognitive Scienceprofile →
Causal Inference in Multisensory Perception
2007739 citationsJoshua B. Tenenbaum et al.profile →
Topics in semantic representation.
2007709 citationsThomas L. Griffiths, Joshua B. Tenenbaum et al.profile →
Countries citing papers authored by Joshua B. Tenenbaum
Since
Specialization
Citations
This map shows the geographic impact of Joshua B. Tenenbaum'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 Joshua B. Tenenbaum with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joshua B. Tenenbaum more than expected).
Fields of papers citing papers by Joshua B. Tenenbaum
This network shows the impact of papers produced by Joshua B. Tenenbaum. 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 Joshua B. Tenenbaum. The network helps show where Joshua B. Tenenbaum may publish in the future.
Co-authorship network of co-authors of Joshua B. Tenenbaum
This figure shows the co-authorship network connecting the top 25 collaborators of Joshua B. Tenenbaum.
A scholar is included among the top collaborators of Joshua B. Tenenbaum 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 Joshua B. Tenenbaum. Joshua B. Tenenbaum is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Puig, Xavier, Tianmin Shu, Shuang Li, et al.. (2021). Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration. arXiv (Cornell University).2 indexed citations
3.
Zhi‐Xuan, Tan, et al.. (2020). Online Bayesian Goal Inference for Boundedly Rational Planning Agents. Neural Information Processing Systems. 33. 19238–19250.1 indexed citations
4.
Tian, Yonglong, Xingyuan Sun, Kevin Ellis, et al.. (2019). Learning to Infer and Execute 3D Shape Programs. DSpace@MIT (Massachusetts Institute of Technology).15 indexed citations
Jara‐Ettinger, Julian, et al.. (2018). Sensitivity to the Sampling Process Emerges From the Principle of Efficiency. OSF Preprints (OSF Preprints).1 indexed citations
7.
Wu, Jiajun, et al.. (2018). Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks.. DSpace@MIT (Massachusetts Institute of Technology). 497–507.2 indexed citations
8.
Siegel, Max, et al.. (2017). Interpreting actions by attributing compositional desires.. Cognitive Science.4 indexed citations
9.
Gerstenberg, Tobias, Matthew Peterson, Noah D. Goodman, David A. Lagnado, & Joshua B. Tenenbaum. (2017). Eye-Tracking Causality. Psychological Science. 28(12). 1731–1744.57 indexed citations
Jara‐Ettinger, Julian, Laura Schulz, & Joshua B. Tenenbaum. (2015). The naïve utility calculus: Joint inferences about the costs and rewards of actions.. Cognitive Science.3 indexed citations
12.
Lin, Dianhuan, et al.. (2014). Bias reformulation for one-shot function induction. DSpace@MIT (Massachusetts Institute of Technology).12 indexed citations
13.
Jara‐Ettinger, Julian, Hyowon Gweon, Joshua B. Tenenbaum, & Laura Schulz. (2014). I’d do anything for a cookie (but I won’t do that): Children’s understanding of the costs and rewards underlying rational action. Cognitive Science. 36(36).1 indexed citations
14.
Gweon, Hyowon, Joshua B. Tenenbaum, & Laura Schulz. (2009). What are you trying to tell me? A Bayesian model of how toddlers can simultaneously infer property extension and sampling processes. DSpace@MIT (Massachusetts Institute of Technology). 31(31).1 indexed citations
15.
Roy, Daniel M., et al.. (2007). Discovering Syntactic Hierarchies. eScholarship (California Digital Library). 29(29).
16.
Goodman, Noah D., Joshua B. Tenenbaum, & Michael J. Black. (2007). A Bayesian Framework for Cross-Situational Word-Learning. DSpace@MIT (Massachusetts Institute of Technology). 20. 457–464.52 indexed citations
17.
Kemp, Charles, Lauren Schmidt, & Joshua B. Tenenbaum. (2006). Nonsense and Sensibility: Inferring Unseen Possibilities. eScholarship (California Digital Library). 28(28).4 indexed citations
18.
Griffiths, Thomas L. & Joshua B. Tenenbaum. (2003). From Algorithmic to Subjective Randomness. Neural Information Processing Systems. 16. 953–960.16 indexed citations
19.
Griffiths, Thomas L. & Joshua B. Tenenbaum. (2000). Teacakes, Trains, Taxicabs and Toxins: A Bayesian Account of Predicting the Future. eScholarship (California Digital Library). 22(22).6 indexed citations
20.
Tenenbaum, Joshua B.. (1995). Learning the Structure of Similarity. Neural Information Processing Systems. 8. 3–9.36 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.