Temporal-kernel recurrent neural networks

Neural Networks : the Official Journal of the International Neural Network Society
Ilya Sutskever, Geoffrey Hinton

Abstract

A Recurrent Neural Network (RNN) is a powerful connectionist model that can be applied to many challenging sequential problems, including problems that naturally arise in language and speech. However, RNNs are extremely hard to train on problems that have long-term dependencies, where it is necessary to remember events for many timesteps before using them to make a prediction. In this paper we consider the problem of training RNNs to predict sequences that exhibit significant long-term dependencies, focusing on a serial recall task where the RNN needs to remember a sequence of characters for a large number of steps before reconstructing it. We introduce the Temporal-Kernel Recurrent Neural Network (TKRNN), which is a variant of the RNN that can cope with long-term dependencies much more easily than a standard RNN, and show that the TKRNN develops short-term memory that successfully solves the serial recall task by representing the input string with a stable state of its hidden units.

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Citations

Aug 13, 2013·Brain Research·Chris HäuslerMartin P Nawrot
May 18, 2017·Journal of Chemical Information and Modeling·Xiuquan DuYanping Zhang
Mar 5, 2019·Australasian Physical & Engineering Sciences in Medicine·Roopa B HegdeBrij Mohan Kumar Singh
Aug 15, 2018·Proceedings of the National Academy of Sciences of the United States of America·Hanlin TangGabriel Kreiman
Jul 20, 2019·Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine·Caohui DuanXin Zhou
Aug 9, 2018·Plant Methods·Sarah Taghavi NaminJustin O Borevitz
Jun 16, 2015·Cognitive Science·Alberto TestolinMarco Zorzi

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