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In this paper we explore how machine learning techniques can be applied to the discovery of efficient mathematical identities.
Semantics of context-free languages
D. E. Knuth · 1968
Earlier work this paper cites.
The complexity of theorem-proving procedures
S. A. Cook · 1971
Earlier work this paper cites.
Symbolic logic and mechanical theorem proving
C.-L. Chang · 1973
Earlier work this paper cites.
Maple V library reference manual
B. W. Char, K. O. Geddes, G. H. Gonnet, B. L. Leong, M. B. Monagan, and S. M. Watt · 1991
Earlier work this paper cites.
Using attribute grammars to find solutions for musical equational programs
M. Desainte-Catherine and K. Barbar · 1994
Earlier work this paper cites.
First-order logic and automated theorem proving
M. Fitting · 1996
Earlier work this paper cites.
The mathematica book
S. Wolfram · 1996
Earlier work this paper cites.
Evolutionary program induction of binary machine code and its applications
P. Nordin · 1997
Earlier work this paper cites.
Evolutionary program induction directed by logic grammars
M. L. Wong and K. S. Leung · 1997
Earlier work this paper cites.
An attribute grammar based framework for machine-dependent computational optimization of media processing algorithms
G. Cheung and S. McCanne · 1999
Earlier work this paper cites.
An approximate matching algorithm for finding (sub-) optimal sequences in s-attributed grammars
J. Waldispühl, B. Behzadi, and J.-M. Steyaert · 2002
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R. Collobert and J. Weston · 2008
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Curriculum learning
Y. Bengio, J. Louradour, R. Collobert, and J. Weston · 2009
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A. Mnih and G. E. Hinton · 2009
Cited alongside, same era.
Learning continuous phrase representations and syntactic parsing with recursive neural networks
R. Socher, C. D. Manning, and A. Y. Ng · 2010
Improving neural networks by preventing co-adaptation of feature detectors
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov · 2012
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S. R. Bowman · 2013
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Efficient estimation of word representations in vector space
T. Mikolov, K. Chen, G. Corrado, and J. Dean · 2013
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
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Enumerative combinatorics
R. P. Stanley · 2011
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Church: a language for generative models
N. Goodman, V. Mansinghka, D. Roy, K. Bonawitz, and D. Tarlow · 2012
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Machine learning for first-order theorem proving
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