Toward Computation and Memory Efficient Neural Network Acoustic Models with Binary Weights and Activations

C. Buciluǎ, R. Caruana, and A. Niculescu-Mizil, “Model compression,” in Proc. ACM SIGKDD , 2006

2006

Earlier work this paper cites.

D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlıcek, Y. Qian, P. Schwarz, J. Silovský, G. Semmer, and K. Veselý, “The Kaldi speech recognition toolkit,” in Proc. ASRU , 2011

2011

Earlier work this paper cites.

J. Duchi, E. Hazan, and Y. Singer, “Adaptive subgradient methods for online learning and stochastic optimization,” Journal of Machine Learning Research , vol. 12, no. Jul, pp. 2121–2159, 2011

Original

2011

Earlier work this paper cites.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” Signal Processing Magazine, IEEE , vol. 29, no. 6, pp. 82–97, 2012

2012

Earlier work this paper cites.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. NIPS , 2012, pp. 1097–1105

2012

Earlier work this paper cites.

R. Pascanu, T. Mikolov, and Y. Bengio, “On the difficulty of training recurrent neural networks,” in Proc. ICML , 2013, pp. 1310–1318

2013

Earlier work this paper cites.

J. Ba and R. Caruana, “Do deep nets really need to be deep?” in Proc. NIPS , 2014, pp. 2654–2662

2014

Earlier work this paper cites.

J. Li, R. Zhao, J.-T. Huang, and Y. Gong, “Learning small-size DNN with output-distribution-based criteria,” in Proc. INTERSPEECH , 2014

2014

Earlier work this paper cites.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research , vol. 15, no. 1, pp. 1929–1958, 2014

2014

Earlier work this paper cites.

D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” in Proc. ICLR , 2015

2015

Cited alongside, same era.

M. Courbariaux, Y. Bengio, and J.-P. David, “Binaryconnect: Training deep neural networks with binary weights during propagations,” in Proc. NIPS , 2015, pp. 3123–3131

2015

Cited alongside, same era.

G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” in Proc. NIPS Deep Learning and Representation Learning Workshop , 2015

2015

Cited alongside, same era.

R. Adriana, B. Nicolas, K. Samira Ebrahimi, C. Antoine, G. Carlo, and B. Yoshua, “Fitnets: Hints for thin deep nets,” in Proc. ICLR , 2015

2015

Cited alongside, same era.

V. Sindhwani, T. N. Sainath, and S. Kumar, “Structured transforms for small-footprint deep learning,” in Proc. NIPS , 2015

2015

Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey et al. , “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation,” in arXiv preprint arXiv:1609.08144 , 2016

Original

2016

Later among the works it cites.

M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio, “Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1,” arXiv preprint arXiv:1602.02830 , 2016

Original

2016

Later among the works it cites.

J. H. Wong and M. J. Gales, “Sequence student-teacher training of deep neural networks,” in Proc. INTERSPEECH . International Speech Communication Association, 2016

2016

Later among the works it cites.

M. Moczulski, M. Denil, J. Appleyard, and N. de Freitas, “ACDC: A Structured Efficient Linear Layer,” in Proc. ICLR , 2016

2016

Later among the works it cites.