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LSTMs have become a basic building block for many deep NLP models.
An empirical evaluation of supervised learning in high dimensions
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Learning word vectors for sentiment analysis
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Baselines and bigrams: Simple, good sentiment and topic classification
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Richard Socher, Alex Perelygin, Jean Y Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
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Distributed representations of sentences and documents
Quoc V Le and Tomas Mikolov · 2014
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Ensemble of generative and discriminative techniques for sentiment analysis of movie reviews
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Glove: Global vectors for word representation
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Cnn features off-the-shelf: An astounding baseline for recognition
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Very deep convolutional networks for large-scale image recognition
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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A theoretically grounded application of dropout in recurrent neural networks
Yarin Gal · 2015
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Deep residual learning for image recognition
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Wojciech Zaremba · 2015
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Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Weinberger · 2016
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Virtual adversarial training for semi-supervised text classification
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Andrej Karpathy, Justin Johnson, and Fei-Fei Li · 2015
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Improved semantic representations from tree-structured long short-term memory networks
Kai Sheng Tai, Richard Socher, and Christopher D Manning · 2015
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Neural tree indexers for text understanding
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Residual networks are exponential ensembles of relatively shallow networks
Andreas Veit, Michael J. Wilber, and Serge J. Belongie · 2016
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Dynamic memory networks for visual and textual question answering
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