Kevin Robert Canini - Publications

Affiliations: 
2011 Computer Science University of California, Berkeley, Berkeley, CA 

7 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2014 Canini KR, Griffiths TL, Vanpaemel W, Kalish ML. Revealing human inductive biases for category learning by simulating cultural transmission. Psychonomic Bulletin & Review. 21: 785-93. PMID 24395094 DOI: 10.3758/s13423-013-0556-3  0.52
2013 Mukherjee I, Canini K, Frongillo R, Singer Y. Parallel boosting with momentum Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8190: 17-32. DOI: 10.1007/978-3-642-40994-3_2  0.52
2012 Griffiths TL, Sanborn AN, Canini KR, Navarro DJ. Categorization as nonparametric Bayesian density estimation The Probabilistic Mind: Prospects For Bayesian Cognitive Science. DOI: 10.1093/acprof:oso/9780199216093.003.0014  0.52
2011 Canini KR, Suh B, Pirolli PL. Finding credible information sources in social networks based on content and social structure Proceedings - 2011 Ieee International Conference On Privacy, Security, Risk and Trust and Ieee International Conference On Social Computing, Passat/Socialcom 2011. 1-8. DOI: 10.1109/PASSAT/SocialCom.2011.91  0.52
2011 Canini KR, Griffiths TL. A nonparametric Bayesian model of multi-level category learning Proceedings of the National Conference On Artificial Intelligence. 1: 307-312.  0.52
2010 Canini KR, Shashkov MM, Griffiths TL. Modeling transfer learning in human categorization with the hierarchical Dirichlet process Icml 2010 - Proceedings, 27th International Conference On Machine Learning. 151-158.  0.52
2009 Canini KR, Shi L, Griffiths TL. Online inference of topics with latent dirichlet allocation Journal of Machine Learning Research. 5: 65-72.  0.52
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