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Deep neural networks have achieved impressive supervised classification performance in many tasks including image recognition, speech recognition, and sequence to sequence learning.
First draft of a report on the EDVAC
Von Neumann, John · 1945
Earlier work this paper cites.
Robust estimation of a location parameter
Huber, Peter · 1964
Earlier work this paper cites.
Backpropagation through time: what does it do and how to do it
Werbos, P · 1990
Earlier work this paper cites.
A self-referentialweight matrix
Schmidhuber, J · 1993
Earlier work this paper cites.
Discrete recurrent neural networks for grammatical inference
Zeng, Z., Goodman, R., and Smyth, P · 1994
Earlier work this paper cites.
A recurrent network that performs a context-sensitive prediction task
Steijvers, Mark · 1996
Earlier work this paper cites.
Learning to parse database queries using inductive logic programming
Zelle, John M. and Mooney, Raymond J · 1996
Earlier work this paper cites.
Long short-term memory
Hochreiter, Sepp and Schmidhuber, Jürgen · 1997
Earlier work this paper cites.
LSTM recurrent networks learn simple context free and context sensitive languages
Gers, Felix A. and Schmidhuber, Jürgen · 2001
Earlier work this paper cites.
Tuning curves for approximate numerosity in the human intraparietal sulcus
Piazza, Manuela, Izard, Veronique, Pinel, Philippe, Le Bihan, Denis, and Dehaene, Stanislas · 2004
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Fias, Wim, Lammertyn, Jan, Caessens, Bernie, and Orban, Guy A · 2007
Earlier work this paper cites.
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Piantadosi, Steven T., Goodman, N.D., Ellis, B.A., and Tenenbaum, J.B · 2008
Earlier work this paper cites.
Reading to learn: Constructing features from semantic abstracts
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
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Cited alongside, same era.
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