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Conventional embedding methods directly associate each symbol with a continuous embedding vector, which is equivalent to applying a linear transformation based on a "one-hot" encoding of the discrete symbols.
Error detecting and error correcting codes
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Building a large annotated corpus of english: The penn treebank
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Long short-term memory
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Recurrent neural network based language model
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Product quantization for nearest neighbor search
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Estimating or propagating gradients through stochastic neurons for conditional computation
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Translating embeddings for modeling multi-relational data
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Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J · 2013
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Low-rank matrix factorization for deep neural network training with high-dimensional output targets
Sainath, T. N., Kingsbury, B., Sindhwani, V., Arisoy, E., and Ramabhadran, B · 2013
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Additive quantization for extreme vector compression
Babenko, A. and Lempitsky, V · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C · 2014
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Recurrent neural network regularization
Zaremba, W., Sutskever, I., and Vinyals, O · 2014
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Character-aware neural language models
Kim, Y., Jernite, Y., Sontag, D., and Rush, A. M · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
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Lightrnn: Memory and computation-efficient recurrent neural networks
Li, X., Qin, T., Yang, J., Hu, X., and Liu, T · 2016
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The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W · 2016
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Task-guided and path-augmented heterogeneous network embedding for author identification
Chen, T. and Sun, Y · 2017
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Chen, W., Wilson, J., Tyree, S., Weinberger, K., and Chen, Y · 2015
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Neural machine translation of rare words with subword units
Sennrich, R., Haddow, B., and Birch, A · 2015
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Entity embedding-based anomaly detection for heterogeneous categorical events
Chen, T., Tang, L.-A., Sun, Y., Chen, Z., and Zhang, K · 2016
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2016
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Han, S., Mao, H., and Dally, W. J
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Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W
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Fasttext. zip: Compressing text classification models
Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., and Mikolov, T
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Chen, T., Min, M. R., and Sun, Y · 2017
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., and Kalenichenko, D · 2017
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Compressing word embeddings via deep compositional code learning
Shu, R. and Nakayama, H · 2017
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Hash embeddings for efficient word representations
Svenstrup, D. T., Hansen, J., and Winther, O · 2017
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