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Discovering the Compositional Structure of Vector Representations with Role Learning Networks

, , , and . (2019)cite arxiv:1910.09113.

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Learnability in Optimality Theory, and . MIT Press, (2000)Tensor Product Production System: A Modular Architecture and Representation, and . Connection Science, (1990)Implementing a Connectionist Production System Using Tensor Products, and . Proceedings of the 1988 Connectionist Models Summer School, page 265--272. San Francisco, CA: Morgan Kaufmann, (1989)The Connectionist Scientist Game: Rule Extraction and Refinement in a Neural Network, , and . CU-CS-530-91. Department of Computer Science and Institute of Cognitive Science, University of Colorado, Boulder, (1992)Connectionism, constituency, and language of thought. chapter 17, page 286--306. Blackwell Publishers, Malden, MA, (1991)Discrete symbolic optimization and Boltzmann sampling by continuous neural dynamics: Gradient Symbolic Computation., , and . CoRR, (2018)Subsymbolic Computation Theory for the Human Intuitive Processor.. CiE, volume 7318 of Lecture Notes in Computer Science, page 675-685. Springer, (2012)Question-Answering with Grammatically-Interpretable Representations., , , and . AAAI, page 5350-5357. AAAI Press, (2018)A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word-Order Universal., and . Cogn. Sci., 36 (8): 1468-1498 (2012)Discovering the Compositional Structure of Vector Representations with Role Learning Networks, , , and . (2019)cite arxiv:1910.09113.