Open Access
Issue |
SHS Web Conf.
Volume 144, 2022
2022 International Conference on Science and Technology Ethics and Human Future (STEHF 2022)
|
|
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Article Number | 02006 | |
Number of page(s) | 5 | |
Section | Mobile Communication Technology and Prospects of Frontier Technology | |
DOI | https://doi.org/10.1051/shsconf/202214402006 | |
Published online | 26 August 2022 |
- Krizhevsky, A., Sutskever, I. & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In F. Pereira, C. J. C. Burges, L. Bottou & K. Q. Weinberger (ed.), Advances in Neural Information Processing Systems 25 (pp. 1097--1105). Curran Associates, Inc.. [Google Scholar]
- Nair, V. & Hinton, G. E. (2010). Rectified Linear Units Improve Restricted Boltzmann Machines. In J. Fürnkranz & T. Joachims (eds.), Proceedings of the 27th International Conference on Machine Learning (ICML-10) (p./pp. 807-814). [Google Scholar]
- Dolezel, P., Skrabanek, P., & Gago, L. (2016). Weight Initialization Possibilities for Feedforward Neural Network with Linear Saturated Activation Functions. IFAC-PapersOnLine, Volume 49, Issue 25, 49-54. https://doi.org/10.1016/j.ifacol.2016.12.009. [CrossRef] [Google Scholar]
- Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013, June). Rectifier nonlinearities improve neural network acoustic models. In Proc. icml (Vol. 30, No. 1, p. 3). [Google Scholar]
- Clevert, D. A., Unterthiner, T., & Hochreiter, S. (2015). Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289. [Google Scholar]
- He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision (pp. 1026-1034). [Google Scholar]
- Arpit, D., & Bengio, Y. (2019). The benefits of overparameterization at initialization in deep ReLU networks. arXiv preprint arXiv:1901.03611. [Google Scholar]
- Gulcehre, C., Moczulski, M., Denil, M., & Bengio, Y. (2016, June). Noisy activation functions. In International conference on machine learning (pp. 3059-3068). PMLR. [Google Scholar]
- Wang, T., Qin, Z., & Zhu, M. (2017, November). An ELU network with total variation for image denoising. In International Conference on Neural Information Processing (pp. 227-237). Springer, Cham. [Google Scholar]
- Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013, June). Rectifier nonlinearities improve neural network acoustic models. In Proc. icml (Vol. 30, No. 1, p. 3). [Google Scholar]
- Klambauer, G., Unterthiner, T., Mayr, A., & Hochreiter, S. (2017, December). Self-normalizing neural networks. In Proceedings of the 31st international conference on neural information processing systems (pp. 972-981). [Google Scholar]
- Zhang, G., & Li, H. (2018). Effectiveness of scaled exponentially-regularized linear units (SERLUs). arXiv preprint arXiv:1807.10117. [Google Scholar]
- Hendrycks, D., & Gimpel, K. (2016). Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415. [Google Scholar]
- Zeiler, M. D., Ranzato, M., Monga, R., Mao, M., Yang, K., Le, Q. V., ... & Hinton, G. E. (2013, May). On rectified linear units for speech processing. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 3517-3521). IEEE. [Google Scholar]
- Dahl, G. E., Sainath, T. N., & Hinton, G. E. (2013, May). Improving deep neural networks for LVCSR using rectified linear units and dropout. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 8609-8613). IEEE. [Google Scholar]
- Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9). [Google Scholar]
- Wen, W., Wu, C., Wang, Y., Chen, Y., & Li, H. (2016). Learning structured sparsity in deep neural networks. Advances in neural information processing systems, 29, 2074-2082. [Google Scholar]
- Kim, H., Khan, M. U. K., & Kyung, C. M. (2019). Efficient neural network compression. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12569-12577). [Google Scholar]
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105. [Google Scholar]
- Wen, T. H., Gasic, M., Mrksic, N., Su, P. H., Vandyke, D., & Young, S. (2015). Semantically conditioned lstm-based natural language generation for spoken dialogue systems. arXiv preprint arXiv:1508.01745. [Google Scholar]
- Chorowski, J., Bahdanau, D., Serdyuk, D., Cho, K., & Bengio, Y. (2015). Attention-based models for speech recognition. arXiv preprint arXiv:1506.07503. [Google Scholar]
- Deng, L. (2012). The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Processing Magazine, 29(6), 141-142. [CrossRef] [Google Scholar]
- Mount, J. (2011). The equivalence of logistic regression and maximum entropy models. URL: http://www.win-vector.com/dfiles/LogisticRegressionMaxEnt.pdf. [Google Scholar]
- Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997). Face recognition: A convolutional neuralnetwork approach. IEEE transactions on neural networks, 8(1), 98-113. [CrossRef] [Google Scholar]
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press. [Google Scholar]
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. [Google Scholar]
- Lin, G., & Shen, W. (2018). Research on convolutional neural network based on improved Relu piecewise activation function. Procedia computer science, 131, 977-984. [CrossRef] [Google Scholar]
- Xu, B., Wang, N., Chen, T., & Li, M. (2015). Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853. [Google Scholar]
- QingJie, W., & WenBin, W. (2017, June). Research on image retrieval using deep convolutional neural network combining L1 regularization and PRelu activation function. In IOP Conference Series: Earth and Environmental Science (Vol. 69, No. 1, p. 012156). IOP Publishing. [Google Scholar]
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