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In recent years significant progress has been made in successfully training recurrent neural networks (RNNs) on sequence learning problems involving long range temporal dependencies.
Learning representations by back-propagating error
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Learning input and recurrent weight matrices in echo state networks
Palangi, H., Deng, Li., and Ward, R.K · 2013
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On the difficulty of training recurrent neural networks
Pascanu, R., Mikolov, T., and Bengio, Y · 2013
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Towards end-to-end speech recognition with recurrent neural networks
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2014
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Zaremba, W. and Sutskever, I · 2014
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The schematic diagram of the work flow for training rnn on ucf-101 benchmark is shown in figure 6
Jain, M., van Gemert, J.C., and Snoek, C · 2015
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Klaus, G., Srivastava, R.K., Koutnik, J., Steunebrink, B.R., and Schmidheuber, J · 2015
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Beyond short snippets: Deep networks for video classification
Ng Yue-Hui, J., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., and Toderici, G · 2015
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Mnist handwritten digit database
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Divide the gradient by a running average of its recent magnitude
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