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Deep ensemble is a simple yet powerful way to improve the performance of deep neural networks.
Learning multiple layers of features from tiny images, 2009
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Distilling the knowledge in a neural network
Hinton, G. E., Vinyals, O., and Dean, J · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 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
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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Snapshot ensembles: Train 1, get M for free
Huang, G., Li, Y., Pleiss, G., Liu, Z., Hopcroft, J. E., and Weinberger, K. Q · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Earlier work this paper cites.
Essentially no barriers in neural network energy landscape
Draxler, F., Veschgini, K., Salmhofer, M., and Hamprecht, F · 2018
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Loss surfaces, mode connectivity, and fast ensembling of DNNs
Garipov, T., Izmailov, P., Podoprikhin, D., Vetrov, D., and Wilson, A. G · 2018
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Averaging weights leads to wider optima and better generalization
Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., and Wilson, A. G · 2018
Cited alongside, same era.
An ensemble with shared representations based on convolutional networks for continually learning facial expressions
Siqueira, H., Barros, P., Magg, S., and Wermter, S · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2018
Cited alongside, same era.
Deep ensembles: A loss landscape perspective
Fort, S., Hu, H., and Lakshminarayanan, B · 2019
Cited alongside, same era.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J. V., Lakshminarayanan, B., and Snoek, J · 2019
Filter response normalization layer: Eliminating batch dependence in the training of deep neural networks
Singh, S. and Krishnan, S · 2020
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Optimizing mode connectivity via neuron alignment
Tatro, N., Chen, P.-Y., Das, P., Melnyk, I., Sattigeri, P., and Lai, R · 2020
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How good is the bayes posterior in deep neural networks really?
Wenzel, F., Roth, K., Veeling, B. S., Świkatkowski, J., Tran, L., Mandt, S., Snoek, J., Salimans, T., Jenatton, R., and Nowozin, S · 2020
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Bayesian deep learning and a probabilistic perspective of generalization
Wilson, A. G. and Izmailov, P · 2020
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Loss surface simplexes for mode connecting volumes and fast ensembling
Benton, G. W., Maddox, W. J., Lotfi, S., and Wilson, A. G · 2021
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The efficiency misnomer
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Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
Cited alongside, same era.
Batchensemble: an alternative approach to efficient ensemble and lifelong learning
Wen, Y., Tran, D., and Ba, J · 2019
Cited alongside, same era.
Depth uncertainty in neural networks
Antorán, J., Allingham, J. U., and Hernández-Lobato, J. M · 2020
Cited alongside, same era.
Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
Ashukha, A., Lyzhov, A., Molchanov, D., and Vetrov, D. P · 2020
Cited alongside, same era.
Efficient and scalable bayesian neural nets with rank-1 factors
Dusenberry, M., Jerfel, G., Wen, Y., Ma, Y., Snoek, J., Heller, K., Lakshminarayanan, B., and Tran, D · 2020
Cited alongside, same era.
Subspace inference for bayesian deep learning
Izmailov, P., Maddox, W. J., Kirichenko, P., Garipov, T., Vetrov, D., and Wilson, A. G · 2020
Cited alongside, same era.
Dehghani, M., Tay, Y., Arnab, A., Beyer, L., and Vaswani, A · 2021
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Training independent subnetworks for robust prediction
Havasi, M., Jenatton, R., Fort, S., Liu, J. Z., Snoek, J., Lakshminarayanan, B., Dai, A. M., and Tran, D · 2021
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What are bayesian neural network posteriors really like?
Izmailov, P., Vikram, S., Hoffman, M. D., and Wilson, A. G · 2021
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Learning neural network subspaces
Wortsman, M., Horton, M., Guestrin, C., Farhadi, A., and Rastegari, M · 2021
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Tiny ImageNet
Li, F.-F., Karpathy, A., and Johnson, J · 2022
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