Paul Smolensky

Johns Hopkins University, Baltimore, MD 
phonology, neural networks, cognitive science, physics
"Paul Smolensky"


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Donald Henry Weingarten grad student 1981 Indiana University (Physics Tree)


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Gaja Jarosz grad student Johns Hopkins
Bruce Tesar grad student 1995 CU Boulder
Matthew Andrew Goldrick grad student 2003 Johns Hopkins
Lisa Davidson grad student 2004 Johns Hopkins
John T. Hale grad student 2004 Johns Hopkins
Adam B. Buchwald grad student 2006 Johns Hopkins
Gaja Jarosz Snover grad student 2007 Johns Hopkins
Sara Finley grad student 2003-2008 Johns Hopkins
Rebecca L. Morley grad student 2009 Johns Hopkins
Deepti Ramadoss grad student 2012 Johns Hopkins
Eric R. Rosen post-doc 2018-2021 Johns Hopkins
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Brehm L, Cho PW, Smolensky P, et al. (2022) PIPS: A Parallel Planning Model of Sentence Production. Cognitive Science. 46: e13079
Russin J, Fernandez R, Palangi H, et al. (2021) Compositional Processing Emerges in Neural Networks Solving Math Problems. Cogsci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference. 2021: 1767-1773
Legendre G, Smolensky P. (2017) A competition-based analysis of French anticausatives Lingvisticæ Investigationes. International Journal of Linguistics and Language Resources. 40: 25-42
Putnam MT, Legendre G, Smolensky P. (2016) How constrained is language mixing in bi- and uni-modal production? Epistemological Issue With Keynote Article “the Development of Bimodal Bilingualism: Implications For Linguistic Theory” by Diane Lillo-Martin, Ronice MüLler De Quadros and Deborah Chen Pichler. 6: 812-816
Smolensky P, Goldrick M, Mathis D. (2014) Optimization and quantization in gradient symbol systems: a framework for integrating the continuous and the discrete in cognition. Cognitive Science. 38: 1102-38
Culbertson J, Smolensky P, Wilson C. (2013) Cognitive biases, linguistic universals, and constraint-based grammar learning. Topics in Cognitive Science. 5: 392-424
Culbertson J, Smolensky P. (2012) A Bayesian model of biases in artificial language learning: the case of a word-order universal. Cognitive Science. 36: 1468-98
Smolensky P. (2012) Symbolic functions from neural computation. Philosophical Transactions. Series a, Mathematical, Physical, and Engineering Sciences. 370: 3543-69
Culbertson J, Smolensky P, Legendre G. (2012) Learning biases predict a word order universal. Cognition. 122: 306-29
Legendre G, Smolensky P. (2012) On the Asymmetrical Difficulty of Acquiring Person Reference in French: Production Versus Comprehension Journal of Logic, Language and Information. 21: 7-30
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