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Combining abstract, symbolic reasoning with continuous neural reasoning is a grand challenge of representation learning.
Neural programming language
Siegelmann, Hava T · 1994
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A neural compiler
Gruau, Frédéric, Ratajszczak, Jean-Yves, and Wiber, Gilles · 1995
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Symbolic processing in neural networks
Neto, João Pedro, Siegelmann, Hava T, and Costa, J Félix · 2003
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Learning a similarity metric discriminatively, with application to face verification
Chopra, Sumit, Hadsell, Raia, and LeCun, Yann · 2005
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Semi-supervised recursive autoencoders for predicting sentiment distributions
Socher, Richard, Pennington, Jeffrey, Huang, Eric H, Ng, Andrew Y, and Manning, Christopher D · 2011
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Practical Bayesian optimization of machine learning algorithms
Snoek, Jasper, Larochelle, Hugo, and Adams, Ryan P · 2012
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Semantic compositionality through recursive matrix-vector spaces
Socher, Richard, Huval, Brody, Manning, Christopher D, and Ng, Andrew Y · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
Socher, Richard, Perelygin, Alex, Wu, Jean Y, Chuang, Jason, Manning, Christopher D, Ng, Andrew Y, and Potts, Christopher · 2013
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DeepMath – Deep sequence models for premise selection
Alemi, Alex A, Chollet, Francois, Irving, Geoffrey, Szegedy, Christian, and Urban, Josef · 2016
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A convolutional attention network for extreme summarization of source code
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Neural GPUs learn algorithms
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Convolutional neural networks over tree structures for programming language processing
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Neural programmer-interpreters
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