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While most deployed speech recognition systems today still run on servers, we are in the midst of a transition towards deployments on edge devices.
Stabilizing the lottery ticket hypothesis
Frankle, J., Dziugaite, K., Roy, D. M., and Carbin, M · 1903
Earlier work this paper cites.
Optimal brain damage
LeCun, Y., Denker, J. S., , and Solla, S. A · 1990
Earlier work this paper cites.
Optimal brain surgeon: Extensions and performance comparisons
Hassibi, B., Stork, D. G., and Wolff, G · 1994
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
Earlier work this paper cites.
Learning to forget: Continual prediction with LSTM
Gers, F. A., Schmidhuber, J., and Cummins, F · 2000
Earlier work this paper cites.
Iterative Methods for Sparse Linear Systems
Saad, Y · 2003
Earlier work this paper cites.
Sequence transduction with recurrent neural networks
Graves, A · 2012
Earlier work this paper cites.
Exploiting sparseness in deep neural networks for large vocabulary speech recognition
Yu, D., Seide, F., Li, G., and Deng, L · 2012
Earlier work this paper cites.
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-r., and Hinton, G · 2013
Earlier work this paper cites.
Speech recognition repair using contextual information, August 19 2014
Chen, L. H · 2014
Earlier work this paper cites.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Cho, K., van Merrienboer, B., Gülçehre, Ç., Bougares, F., Schwenk, H., and Bengio, Y · 2014
Earlier work this paper cites.
A 240 g-ops/s mobile coprocessor for deep neural networks
Gokhale, V., Jin, J., Dundar, A., Martini, B., and Culurciello, E · 2014
Earlier work this paper cites.
Long short-term memory recurrent neural network architectures for large scale acoustic modeling
Sak, H., Senior, A., and Beaufays, F · 2014
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., , and Dally, W · 2015
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An empirical exploration of recurrent network architectures
Jozefowicz, R., Zaremba, W., and Sutskever, I · 2015
Cited alongside, same era.
On the efficient representation and execution of deep acoustic models
Alvarez, R., Prabhavalkar, R., and Bakhtin, A · 2016
Cited alongside, same era.
Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
Cited alongside, same era.
Listen, attend and spell: A neural network for large vocabulary conversational speech recognition
Chan, W., Jaitly, N., Le, Q., and Vinyals, O · 2016
Cited alongside, same era.
Mixed low-precision deep learning inference using dynamic fixed point
Mellempudi, N., Kundu, A., Das, D., Mudigere, D., and Kaul, B · 2017
Later among the works it cites.
Incremental network quantization: Towards lossless cnns with low-precision weights
Zhou, A., Yao, A., Guo, Y., Xu, L., and Chen, Y · 2017
Later among the works it cites.
State-of-the-art speech recognition with sequence-to-sequence models
Chiu, C.-C., Sainath, T. N., Wu, Y., Prabhavalkar, R., Nguyen, P., Chen, Z., Kannan, A., Weiss, R. J., Rao, K., Gonina, E., et al · 2018
Later among the works it cites.
Simple recurrent units for highly parallelizable recurrence
Lei, T., Zhang, Y., Wang, S. I., Dai, H., and Artzi, Y · 2018
Later among the works it cites.
Fully neural network based speech recognition on mobile and embedded devices
Park, J., Boo, Y., Choi, I., Shin, S., and Sung, W · 2018
Later among the works it cites.
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Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., and Bengio, Y · 2016
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Lstm: A search space odyssey
Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., and Schmidhuber, J · 2016
Cited alongside, same era.
Dsd: Dense-sparse-dense training for deep neural networks
Han, S., Pool, J., Narang, S., Mao, H., Gong, E., Tang, S., Elsen, E., Vajda, P., Paluri, M., Tran, J., et al · 2016
Cited alongside, same era.
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Jaitly, N., Le, Q. V., Vinyals, O., Sutskever, I., Sussillo, D., and Bengio, S · 2016
Cited alongside, same era.
Exploring the limits of language modeling, 2016
Jozefowicz, R., Vinyals, O., Schuster, M., Shazeer, N., and Wu, Y · 2016
Cited alongside, same era.
Personalized speech recognition on mobile devices
McGraw, I., Prabhavalkar, R., Alvarez, R., Arenas, M. G., Rao, K., Rybach, D., Alsharif, O., Sak, H., Gruenstein, A., Beaufays, F., and Parada, C · 2016
Cited alongside, same era.
Quantization and training of neural networks for efficient integer-arithmetic-only inference
Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A. G., Adam, H., and Kalenichenko, D · 2017
Cited alongside, same era.
The unreasonable effectiveness of the forget gate
van der Westhuizen, J. and Lasenby, J · 2018
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To prune, or not to prune: exploring the efficacy of pruning for model compression
Zhu, M. and Gupta, S · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
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Streaming end-to-end speech recognition for mobile devices
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Snip: Single-shot network pruning based on connection sensitivity
Lee, N., Ajanthan, T., , and Torr, P. H · 2019
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Rethinking the value of network pruning
Liu, Z., Sun, M., Zhou, T., Huang, G., , and Darrell, T · 2019
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Recognizing long-form speech using streaming end-to-end models
Narayanan, A., Prabhavalkar, R., Chiu, C.-C., Rybach, D., Sainath, T. N., and Strohman, T · 2019
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