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Recurrent neural networks show state-of-the-art results in many text analysis tasks but often require a lot of memory to store their weights.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Pang, Bo and Lee, Lillian · 2005
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Extensions of recurrent neural network language model
Mikolov, T., Kombrink, S., Burget, L., Cernocky, J.H., and Khudanpur, Sanjeev · 2011
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On fast dropout and its applicability to recurrent networks
Bayer, Justin, Osendorfer, Christian, Chen, Nutan, Urban, Sebastian, and van der Smagt, Patrick · 2013
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Auto-encoding variational bayes
Kingma, Diederik P. and Welling, Max · 2013
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Dropout improves recurrent neural networks for handwriting recognition
Pham, Vu, Kermorvant, Christopher, and Louradour, Jérôme · 2013
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Improving neural networks with dropout
Srivastava, Nitish · 2013
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Fast dropout training
Wang, Sida and Manning, Christopher · 2013
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Recurrent neural network regularization
Zaremba, Wojciech, Sutskever, Ilya, and Vinyals, Oriol · 2014
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Lasagne: First release., 2015
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Adam: A method for stochastic optimization
Kingma, Diederik P. and Ba, Jimmy · 2015
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Variational dropout and the local reparameterization trick
Kingma, Diederik P., Salimans, Tim, and Welling, Max · 2015
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A simple way to initialize recurrent networks of rectified linear units
Le, Quoc V., Jaitly, Navdeep, and Hinton, Geoffrey E · 2015
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Rnndrop: A novel dropout for rnns in asr
Moon, Taesup, Choi, Heeyoul, Lee, Hoshik, and Song, Inchul · 2015
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Exploring models and data for image question answering
Ren, Mengye, Kiros, Ryan, and Zemel, Richard S · 2015
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Deep speech 2 : End-to-end speech recognition in english and mandarin
Amodei, Dario, Ananthanarayanan, Sundaram, Anubhai, Rishita, and et al · 2016
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Recurrent dropout without memory loss
Semeniuta, Stanislau, Severyn, Aliaksei, and Barth, Erhardt · 2016
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Theano: A Python framework for fast computation of mathematical expressions
Theano Development Team · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Wu, Yonghui, Schuster, Mike, Chen, Zhifeng, and et al · 2016
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Bayesian compression for deep learning
Christos Louizos, Karen Ullrich, Max Welling · 2017
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Hypernetworks
Ha, David, Dai, Andrew, and Le, Quoc V · 2017
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Listen, attend and spell: A neural network for large vocabulary conversational speech recognition
Chan, William, Jaitly, Navdeep, Le, Quoc V., and Vinyals, Oriol · 2016
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Deep networks with stochastic depth
Huang, Gao, Sun, Yu, Liu, Zhuang, Sedra, Daniel, and Weinberger, Kilian Q · 2016
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Quantized neural networks: Training neural networks with low precision weights and activations
Hubara, Itay, Courbariaux, Matthieu, Soudry, Daniel, El-Yaniv, Ran, and Bengio, Yoshua · 2016
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Zoneout: Regularizing rnns by randomly preserving hidden activations
Krueger, David, Maharaj, Tegan, Kramár, János, and et al · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Yarin and Ghahramani, Zoubin
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A theoretically grounded application of dropout in recurrent neural networks
Gal, Yarin and Ghahramani, Zoubin
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Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha Dmitry Vetrov · 2017
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Variational dropout sparsifies deep neural networks
Molchanov, Dmitry, Ashukha, Arsenii, and Vetrov, Dmitry · 2017
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Exploring sparsity in recurrent neural networks
Narang, Sharan, Diamos, Gregory F., Sengupta, Shubho, and Elsen, Erich · 2017
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Compressing recurrent neural network with tensor train
Tjandra, Andros, Sakti, Sakriani, and Nakamura, Satoshi · 2017
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