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Ensembles of deep neural networks are known to achieve state-of-the-art performance in uncertainty estimation and lead to accuracy improvement.
A constructive prediction of the generalization error across scales
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Deep ensembles: A loss landscape perspective
Fort, S., Hu, H., and Lakshminarayanan, B. (2019) · 1912
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Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014) · 1958
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Scaling laws for neural language models
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The case for bayesian deep learning
Wilson, A. G. (2020) · 2001
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Bayesian deep learning and a probabilistic perspective of generalization
Wilson, A. G. and Izmailov, P. (2020) · 2002
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Double trouble in double descent: Bias and variance (s) in the lazy regime
d’Ascoli, S., Refinetti, M., Biroli, G., and Krzakala, F. (2020) · 2003
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Dibs: Diversity inducing information bottleneck in model ensembles
Sinha, S., Bharadhwaj, H., Goyal, A., Larochelle, H., Garg, A., and Shkurti, F. (2020) · 2003
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Neural ensemble search for performant and calibrated predictions
Zaidi, S., Zela, A., Elsken, T., Holmes, C., Hutter, F., and Teh, Y. W. (2020) · 2006
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Bayesian deep ensembles via the neural tangent kernel
He, B., Lakshminarayanan, B., and Teh, Y. W. (2020) · 2007
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A. (2014) · 2014
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2014) · 2014
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Stochastic multiple choice learning for training diverse deep ensembles
Lee, S., Prakash, S. P. S., Cogswell, M., Ranjan, V., Crandall, D., and Batra, D. (2016) · 2016
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Wide residual networks
Zagoruyko, S. and Komodakis, N. (2016) · 2016
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C. (2017) · 2017
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Coupled ensembles of neural networks
Dutt, A., Pellerin, D., and Quénot, G. (2018) · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Jacot, A., Gabriel, F., and Hongler, C. (2018) · 2018
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On exact computation with an infinitely wide neural net
Arora, S., Du, S. S., Hu, W., Li, Z., Salakhutdinov, R. R., and Wang, R. (2019) · 2019
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Reconciling modern machine-learning practice and the classical bias–variance trade-off
Belkin, M., Hsu, D., Ma, S., and Mandal, S. (2019) · 2019
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Jamming transition as a paradigm to understand the loss landscape of deep neural networks
Geiger, M., Spigler, S., d’Ascoli, S., Sagun, L., Baity-Jesi, M., Biroli, G., and Wyart, M. (2019) · 2019
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Wide neural networks of any depth evolve as linear models under gradient descent
Lee, J., Xiao, L., Schoenholz, S., Bahri, Y., Novak, R., Sohl-Dickstein, J., and Pennington, J. (2019) · 2019
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Ensemblenet: End-to-end optimization of multi-headed models
Li, H., Ng, J. Y.-H., and Natsev, P. (2019) · 2019
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The relative performance of ensemble methods with deep convolutional neural networks for image classification
Ju, C., Bibaut, A., and van der Laan, M. (2018) · 2018
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Attention-based ensemble for deep metric learning
Kim, W., Goyal, B., Chawla, K., Lee, J., and Kwon, K. (2018) · 2018
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A modern take on the bias-variance tradeoff in neural networks
Neal, B., Mittal, S., Baratin, A., Tantia, V., Scicluna, M., Lacoste-Julien, S., and Mitliagkas, I. (2018) · 2018
Cited alongside, same era.
Towards understanding the role of over-parametrization in generalization of neural networks
Neyshabur, B., Li, Z., Bhojanapalli, S., LeCun, Y., and Srebro, N. (2018) · 2018
Cited alongside, same era.
Sensitivity and generalization in neural networks: an empirical study
Novak, R., Bahri, Y., Abolafia, D. A., Pennington, J., and Sohl-Dickstein, J. (2018) · 2018
Cited alongside, same era.
CIFAR-10 (canadian institute for advanced research)
Krizhevsky, A., Nair, V., and Hinton, G
Cited in the paper.
Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
Ashukha, A., Lyzhov, A., Molchanov, D., and Vetrov, D. (2020) · 2020
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Deep ensembles on a fixed memory budget: One wide network or several thinner ones?
Chirkova, N., Lobacheva, E., and Vetrov, D. (2020) · 2020
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Scaling description of generalization with number of parameters in deep learning
Geiger, M., Jacot, A., Spigler, S., Gabriel, F., Sagun, L., d’Ascoli, S., Biroli, G., Hongler, C., and Wyart, M. (2020) · 2020
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When ensembling smaller models is more efficient than single large models
Kondratyuk, D., Tan, M., Brown, M., and Gong, B. (2020) · 2020
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Deep double descent: Where bigger models and more data hurt
Nakkiran, P., Kaplun, G., Bansal, Y., Yang, T., Barak, B., and Sutskever, I. (2020) · 2020
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