On the Emergence of Rules in Neural Networks
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[1] John R. Vokey,et al. Abstract analogies and abstracted grammars: Comments on Reber and Mathews et al. , 1991 .
[2] Michael McCloskey,et al. Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem , 1989 .
[3] Paul Thagard,et al. Coherence as Constraint Satisfaction , 2019, Cogn. Sci..
[4] Gerry Altmann,et al. Mapping across Domains Without Feedback: A Neural Network Model of Transfer of Implicit Knowledge , 1999, Cogn. Sci..
[5] J. Elman. Distributed Representations, Simple Recurrent Networks, And Grammatical Structure , 1991 .
[6] R N Aslin,et al. Statistical Learning by 8-Month-Old Infants , 1996, Science.
[7] Peter M. Vishton,et al. Rule learning by seven-month-old infants. , 1999, Science.
[8] J. Fodor,et al. Connectionism and cognitive architecture: A critical analysis , 1988, Cognition.
[9] Michael I. Jordan. Serial Order: A Parallel Distributed Processing Approach , 1997 .
[10] Mike Casey,et al. The Dynamics of Discrete-Time Computation, with Application to Recurrent Neural Networks and Finite State Machine Extraction , 1996, Neural Computation.
[11] Lorien Y. Pratt,et al. Discriminability-Based Transfer between Neural Networks , 1992, NIPS.
[12] J. Berko. The Child's Learning of English Morphology , 1958 .
[13] N. Chater,et al. Transfer in artificial grammar learning : A reevaluation , 1996 .
[14] Axel Cleeremans,et al. Mechanisms of Implicit Learning: Connectionist Models of Sequence Processing , 1993 .
[15] C. Lee Giles,et al. Learning a class of large finite state machines with a recurrent neural network , 1995, Neural Networks.
[16] A. Reber. Implicit learning of artificial grammars , 1967 .
[17] S. Pinker. The Language Instinct , 1994 .
[18] Christian W. Omlin,et al. A Machine Learning Method for Extracting Symbolic Knowledge from Recurrent Neural Networks , 2004, Neural Computation.
[19] S Pinker,et al. Rules of language. , 1991, Science.
[20] Stephen Jose Hanson,et al. Grammar Transfer in a Second Order Recurrent Neural Network , 2001, NIPS.
[21] Jack Mostow,et al. Direct Transfer of Learned Information Among Neural Networks , 1991, AAAI.
[22] Geoffrey E. Hinton,et al. Learning representations by back-propagation errors, nature , 1986 .
[23] Ronald J. Williams,et al. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.
[24] A. Reber. Transfer of syntactic structure in synthetic languages. , 1969 .
[25] Lawrence D. Jackel,et al. Large Automatic Learning, Rule Extraction, and Generalization , 1987, Complex Syst..
[26] N. Chater,et al. Computational models and Rethinking innateness , 1999, Journal of Child Language.
[27] Stephen José Hanson,et al. What connectionist models learn: Learning and representation in connectionist networks , 1990, Behavioral and Brain Sciences.
[28] John R. Vokey,et al. Abstract analogies and abstracted grammars: Comments on Reber (1989) and Mathews et al. (1989). , 1991 .
[29] S. Pinker. How the Mind Works , 1999, Annals of the New York Academy of Sciences.
[30] C. Lee Giles,et al. Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks , 1992, Neural Computation.
[31] Stephen José Hanson,et al. A stochastic version of the delta rule , 1990 .
[32] Jeffrey L. Elman,et al. Finding Structure in Time , 1990, Cogn. Sci..
[33] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.