Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks-Reference-Cited by-同舟云学术

Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks

Published:2021 Issue: Volume: Page:111-126
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
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Author:

Shekhovtsov Alexander,Yanush Viktor

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-92659-5_7

Reference60 articles.

1. Ajanthan, T., Gupta, K., Torr, P.H., Hartley, R., Dokania, P.K.: Mirror descent view for neural network quantization. arXiv preprint arXiv:1910.08237 (2019)

2. Alizadeh, M., Fernandez-Marques, J., Lane, N.D., Gal, Y.: An empirical study of binary neural networks’ optimisation. In: ICLR (2019)

3. Azizan, N., Lale, S., Hassibi, B.: A study of generalization of stochastic mirror descent algorithms on overparameterized nonlinear models. In: ICASSP, pp. 3132–3136 (2020)

4. Bai, Y., Wang, Y.-X., Liberty, E.: ProxQuant: quantized neural networks via proximal operators. In: ICLR (2019)

5. Bengio, Y., Léonard, N., Courville, A.: Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013)

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