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Dropout methods are a family of stochastic techniques used in neural network training or inference that have generated significant research interest and are widely used in practice.
2012
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2013
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2014
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D. Warde-Farley, I. J. Goodfellow, A. Courville, and Y. Bengio, “An empirical analysis of dropout in piecewise linear networks,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2014
2014
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2014
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2015
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2015
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T. Moon, H. Choi, H. Lee, and I. Song, “Rnndrop: A novel dropout for RNNs in ASR,” in 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) . IEEE, 2015
2015
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2015
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2015
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2015
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2015
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Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in Proceedings of the 33rd International Conference on Machine Learning . PLMR, 2016
2016
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2016
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2016
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2017
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2017
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2017
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2017
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Y. Gal and Z. Ghahramani, “A theoretically grounded application of dropout in recurrent neural networks,” in Proceedings of the 30th International Conference on Neural Information Processing Systems . NIPS, 2016
2016
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S. Semeniuta, A. Severyn, and E. Barth, “Recurrent dropout without memory loss,” in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics , 2016
2016
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2016
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Z. Li, B. Gong, and T. Yang, “Improved dropout for shallow and deep learning,” in Proceedings of the 30th International Conference on Neural Information Processing Systems . NIPS, 2016
2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE CVPR , 2016, pp. 770–778
2016
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S. Singh, D. Hoiem, and D. Forsyth, “Swapout: Learning an ensemble of deep architectures,” in Proceedings of the 30th International Conference on Neural Information Processing Systems . NIPS, 2016
2016
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S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2016
2016
Cited alongside, same era.
Y. Gal, “Uncertainty in deep learning,” Ph.D. dissertation, University of Cambridge, 2016
2016
Cited alongside, same era.
2017
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A. N. Gomez, I. Zhang, K. Swersky, Y. Gal, and G. E. Hinton, “Targeted dropout,” in 2018 CDNNRIA Workshop at the 32nd Conference on Neural Information Processing Systems . NeurIPS, 2018
2018
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2018
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A. Achille and S. Soatto, “Information dropout,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 40, no. 12, pp. 2897–2905, 2018
2018
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A. Jungo, R. McKinley, R. Meier, U. Knecht, L. Vera, J. Pérez-Beteta, D. Molina-García, V. M. Pérez-García, R. Wiest, and M. Reyes, “Towards uncertainty-assisted brain tumor segmentation and survival prediction,” in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries , A. Crimi, S. Bakas, H. Kuijf, B. Menze, and M. Reyes, Eds. Cham: Springer International Publishing, 2018, pp. 474–485
2018
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S. Park, J. Park, S.-J. Shin, and I.-C. Moon, “Adversarial dropout for supervised and semi-supervised learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2018
2018
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2018
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2019
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2019
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S. H. Khan, M. Hayat, and F. Porikli, “Regularization of deep neural networks with spectral dropout,” Neural Networks , vol. 110, pp. 82–90, 2019
2019
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S. Hou and Z. Wang, “Weighted channel dropout for regularization of deep convolutional neural network,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2019
2019
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2019
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