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Recurrent neural networks (RNNs) can learn continuous vector representations of symbolic structures such as sequences and sentences; these representations often exhibit linear regularities (analogies).
Context-sensitive coding, associative memory, and serial order in (speech) behavior
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Allen Newell · 1980
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Connectionism and cognitive architecture: A critical analysis
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M. Alex O. Vasilescu and Demetri Terzopoulos · 2005
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Beyond mind-reading: multi-voxel pattern analysis of fMRI data
Kenneth A. Norman, Sean M. Polyn, Greg J. Detre, and James V. Haxby · 2006
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Representation of letter position in spelling: Evidence from acquired dysgraphia
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Richard Socher, Christopher D. Manning, and Andrew Y. Ng · 2010
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Learning phrase representations using RNN encoder–decoder for statistical machine translation
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Huadong Chen, Shujian Huang, David Chiang, and Jiajun Chen · 2017
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Supervised learning of universal sentence representations from natural language inference data
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