Joshua B. Tenenbaum
Impact in
- General Decision Sciences top 0.1%
- Developmental and Educational Psychology top 0.05%
- Child and Animal Learning Development
Papers in
-
- Bayesian Modeling and Causal Inference 61
- Topic Modeling 32
- Machine Learning and Algorithms 26
- Reinforcement Learning in Robotics 26
- Natural Language Processing Techniques 25
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- Child and Animal Learning Development 107
- Co-authors
- Vin de Silva (1 shared paper)John Langford (1 shared paper)Thomas L. Griffiths (46 shared papers)Charles Kemp (29 shared papers)Noah D. Goodman (47 shared papers)Mark Steyvers (6 shared papers)Ruslan Salakhutdinov (7 shared papers)Brenden M. Lake (6 shared papers)
- Journals
- Cognitive Science (53 papers)Cognition (19 papers)Trends in Cognitive Sciences (11 papers)Proceedings of the National Academy of Sciences (10 papers)Psychological Review (10 papers)
- Partner nations
- United StatesUnited KingdomChina
In The Last Decade
Joshua B. Tenenbaum
402 papers receiving 32.6k citations
Joshua B. Tenenbaum's Hit Papers
Peers
Comparison fields: 5 of 225
- General Decision Sciences 1.5k
- Developmental and Educational Psychology 6.5k
- Computer Vision and Pattern Recognition 9.2k
- Artificial Intelligence 14.0k
- Cognitive Neuroscience 7.0k
Countries citing papers authored by Joshua B. Tenenbaum
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-authors
The 25 scholars most cited alongside Joshua B. Tenenbaum, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 420 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | A Global Geometric Framework for Nonlinear Dimensionality Reduction Hit paper breakdown → | 2000 | 8822 |
| 2 | Human-level concept learning through probabilistic program induction Hit paper breakdown → | 2015 | 1395 |
| 3 | How to Grow a Mind: Statistics, Structure, and Abstraction Hit paper breakdown → | 2011 | 1043 |
| 4 | The Large‐Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth Hit paper breakdown → | 2005 | 859 |
| 5 | Causal Inference in Multisensory Perception Hit paper breakdown → | 2007 | 747 |
| 6 | Topics in semantic representation. Hit paper breakdown → | 2007 | 714 |
| 7 | Hierarchical Topic Models and the Nested Chinese Restaurant Process Hit paper breakdown → | 2003 | 597 |
| 8 | Word learning as Bayesian inference. Hit paper breakdown → | 2007 | 553 |
| 9 | Separating Style and Content with Bilinear Models Hit paper breakdown → | 2000 | 550 |
| 10 | Action understanding as inverse planning Hit paper breakdown → | 2009 | 539 |
| 11 | Global Versus Local Methods in Nonlinear Dimensionality Reduction Hit paper breakdown → | 2002 | 509 |
| 12 | Theory-based Bayesian models of inductive learning and reasoning Hit paper breakdown → | 2006 | 505 |
| 13 | 2001 | 480 | |
| 14 | 2005 | 386 | |
| 15 | Simulation as an engine of physical scene understanding Hit paper breakdown → | 2013 | 375 |
| 16 | 2006 | 361 | |
| 17 | Computational rationality: A converging paradigm for intelligence in brains, minds, and machines Hit paper breakdown → | 2015 | 361 |
| 18 | Learning systems of concepts with an infinite relational model | 2006 | 358 |
| 19 | 2010 | 351 | |
| 20 | Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation Hit paper breakdown → | 2016 | 331 |
About Joshua B. Tenenbaum
Joshua B. Tenenbaum is a scholar working on Artificial Intelligence, Developmental and Educational Psychology, Cognitive Neuroscience, Computer Vision and Pattern Recognition and Cultural Studies, having authored 420 papers that have together received 34.7k indexed citations. Recurring topics across this work include Child and Animal Learning Development (107 papers), Bayesian Modeling and Causal Inference (61 papers), Language and cultural evolution (47 papers), Topic Modeling (32 papers), Decision-Making and Behavioral Economics (29 papers), Machine Learning and Algorithms (26 papers), Reinforcement Learning in Robotics (26 papers) and Natural Language Processing Techniques (25 papers). The work is most often cited by research in General Decision Sciences (1.5k citations), Developmental and Educational Psychology (6.5k citations), Computer Vision and Pattern Recognition (9.2k citations), Artificial Intelligence (14.0k citations) and Cognitive Neuroscience (7.0k citations). Joshua B. Tenenbaum has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Vin de Silva, John Langford, Thomas L. Griffiths, Charles Kemp, Noah D. Goodman, Mark Steyvers, Ruslan Salakhutdinov, Brenden M. Lake, Fei Xu and Rebecca Saxe. Their work appears in journals such as Cognitive Science, Cognition, Trends in Cognitive Sciences, Proceedings of the National Academy of Sciences and Psychological Review.
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.