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End-to-End training (E2E) is becoming more and more popular to train complex Deep Network architectures.
Dropout: A simple way to prevent neural networks from overfitting
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Stacked generalization
Wolpert, D. H. (1992) · 1992
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Bagging predictors
Breiman, L. (1996) · 1996
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Selecting weighting factors in logarithmic opinion pools
Heskes, T. (1998) · 1998
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Ensemble learning via negative correlation
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Ensemble methods in machine learning
Dietterich, T. G. (2000) · 2000
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Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
Kuncheva, L. I. and Whitaker, C. J. (2003) · 2003
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Product of experts
Welling, M. (2007) · 2007
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Learning multiple layers of features from tiny images
Krizhevsky, A. (2009) · 2009
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J. (2015) · 2015
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Why m heads are better than one: Training a diverse ensemble of deep networks
Lee, S., Purushwalkam, S., Cogswell, M., Crandall, D., and Batra, D. (2015) · 2015
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Residual networks behave like ensembles of relatively shallow networks
Veit, A., Wilber, M. J., and Belongie, S. (2016) · 2016
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On the connection of deep fusion to ensembling
Zhao, L., Wang, J., Li, X., Tu, Z., and Zeng, W. (2016) · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017) · 2017
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Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R. (2017) · 2017
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Leveraging translations for speech transcription in low-resource settings
Anastasopoulos, A. and Chiang, D. (2018) · 2018
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Furlanello, T., Lipton, Z. C., Tschannen, M., Itti, L., and Anandkumar, A. (2018) · 2018
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Multi-scale dense networks for resource efficient image classification
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Glasmachers, T. (2017) · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., v. d. Maaten, L., and Weinberger, K. Q. (2017) · 2017
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Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K. (2017) · 2017
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A PyTorch implementation of DenseNet
Amos, B. and Kolter, J. Z. (2017) · 2017
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Diversity creation methods: a survey and categorisation
Brown, G., Wyatt, J., Harris, R., and Yao, X. (2005a)
Cited in the paper.
Managing diversity in regression ensembles
Brown, G., Wyatt, J. L., and Tiňo, P. (2005b)
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Huang, G., Chen, D., Li, T., Wu, F., van der Maaten, L., and Weinberger, K. (2018) · 2018
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Modular dimensionality reduction
Reeve, H., Mu, T., and Brown, G. (2018) · 2018
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A PyTorch implementation of Coupled Ensembles
Dutt, A., Pellerin, D., and Quénot, G. (2018) · 2018
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Binary ensemble neural network: More bits per network or more networks per bit?
Zhu, S., Dong, X., and Su, H. (2019) · 2019
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Coupled ensembles of neural networks
Dutt, A., Pellerin, D., and Quénot, G. (July 2020) · 2020
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