Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting

Turing computability with neural nets

Hava T. Siegelmann and Eduardo D. Sontag · 1991

Earlier work this paper cites.

Spectral networks and locally connected networks on graphs

Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2014

Earlier work this paper cites.

Short-term traffic forecasting: Where we are and where we’re going

Eleni I. Vlahogianni, Matthew G. Karlaftis, and John C. Golias · 2014

Earlier work this paper cites.

End-to-end memory networks

Sainbayar Sukhbaatar, arthur szlam, Jason Weston, and Rob Fergus · 2015

Earlier work this paper cites.

Memory networks

Jason Weston, Sumit Chopra, and Antoine Bordes · 2015

Earlier work this paper cites.

Dnn-based prediction model for spatio-temporal data

Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, and Xiuwen Yi · 2016

Earlier work this paper cites.

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Lukasz Kaiser, Ofir Nachum, Aurko Roy, and Samy Bengio · 2017

Earlier work this paper cites.

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