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Sequential models achieve state-of-the-art results in audio, visual and textual domains with respect to both estimating the data distribution and generating high-quality samples.
Inter-block GPU communication via fast barrier synchronization
Xiao, S. and c. Feng, W · 2010
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J., Gülçehre, Ç., Cho, K., and Bengio, Y · 2014
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Persistent RNNs: Stashing recurrent weights on-chip
Diamos, G., Sengupta, S., Catanzaro, B., Chrzanowski, M., Coates, A., Elsen, E., Engel, J., Hannun, A., and Satheesh, S · 2016
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Optimizing RNNs with differentiable graphs, June 2016
Engel, J · 2016
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Neural machine translation in linear time
Kalchbrenner, N., Espeholt, L., Simonyan, K., van den Oord, A., Graves, A., and Kavukcuoglu, K · 2016
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SampleRNN: an unconditional end-to-end neural audio generation model
Mehri, S., Kumar, K., Gulrajani, I., Kumar, R., Jain, S., Sotelo, J., Courville, A. C., and Bengio, Y · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., and al · 2016
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Neural audio synthesis of musical notes with wavenet autoencoders
Engel, J., Resnick, C., Roberts, A., Dieleman, S., Eck, D., Simonyan, K., and Norouzi, M · 2017
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MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
Gordon, A., Eban, E., Nachum, O., Chen, B., Wu, H., Yang, T.-J., and Choi, E · 2017
Cited alongside, same era.
Block-sparse gpu kernels, Dec 2017
Gray, S., Radford, A., and Kingma, D · 2017
Cited alongside, same era.
Non-autoregressive neural machine translation
Gu, J., Bradbury, J., Xiong, C., Li, V. O. K., and Socher, R · 2017
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Video pixel networks
Kalchbrenner, N., van den Oord, A., Simonyan, K., Danihelka, I., Vinyals, O., Graves, A., and Kavukcuoglu, K · 2017
Cited alongside, same era.
Performance RNN: Generating music with expressive timing and dynamics
Simon, I. and Oore, S · 2017
Later among the works it cites.
Parallel WaveNet: Fast high-fidelity speech synthesis
van den Oord, A., Li, Y., Babuschkin, I., Simonyan, K., Vinyals, O., Kavukcuoglu, K., van den Driessche, G., Lockhart, E., Cobo, L. C., Stimberg, F., Casagrande, N., Grewe, D., Noury, S., Dieleman, S., Elsen, E., Kalchbrenner, N., Zen, H., Graves, A., King, H., Walters, T., Belov, D., and Hassabis, D · 2017
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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Tacotron: A fully end-to-end text-to-speech synthesis model
Wang, Y., Skerry-Ryan, R. J., Stanton, D., Wu, Y., Weiss, R. J., Jaitly, N., Yang, Z., Xiao, Y., Chen, Z., Bengio, S., Le, Q. V., Agiomyrgiannakis, Y., Clark, R., and Saurous, R. A · 2017
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Parallel multiscale autoregressive density estimation
Reed, S. E., van den Oord, A., Kalchbrenner, N., Colmenarejo, S. G., Wang, Z., Chen, Y., Belov, D., and de Freitas, N · 2017
Cited alongside, same era.
Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P · 2017
Cited alongside, same era.
URL https://browser.geekbench.com/v4/cpu/6960655
geekbench, 2018a
Cited in the paper.
URL https://browser.geekbench.com/v4/cpu/6473830
geekbench, 2018b
Cited in the paper.
Exploring sparsity in recurrent neural networks
Narang, S., Diamos, E. E. G. F., and Sengupta, S
Cited in the paper.
Block-sparse recurrent neural networks
Narang, S., Undersander, E., and Diamos, G. F
Cited in the paper.
WaveNet: A generative model for raw audio
van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K
Cited in the paper.
Zhu, M. and Gupta, S · 2017
Later among the works it cites.
Viterbi-based pruning for sparse matrix with fixed and high index compression ratio
Lee, D., Ahn, D., Kim, T., Chuang, P. I., and Kim, J.-J · 2018
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