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Accurate estimation of predictive uncertainty (model calibration) is essential for the safe application of neural networks.
Verification of forecasts expressed in terms of probability
Brier, G. W · 1950
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The comparison and evaluation of forecasters
DeGroot, M. H. and Fienberg, S. E · 1983
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., et al · 2006
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Probabilistic forecasts, calibration and sharpness
Gneiting, T., Balabdaoui, F., and Raftery, A. E · 2007
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A tutorial on conformal prediction
Shafer, G. and Vovk, V · 2008
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Reliability, sufficiency, and the decomposition of proper scores
Bröcker, J · 2009
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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A unified view of performance metrics: translating threshold choice into expected classification loss
Hernández-Orallo, J., Flach, P. A., and Ferri, C · 2012
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Calibrating predictive model estimates to support personalized medicine
Jiang, X., Osl, M., Kim, J., and Ohno-Machado, L · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Bayesian data analysis
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B · 2013
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One weird trick for parallelizing convolutional neural networks
Krizhevsky, A · 2014
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Obtaining well calibrated probabilities using bayesian binning
Naeini, M. P., Cooper, G., and Hauskrecht, M · 2015
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End to end learning for self-driving cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al · 2016
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., and Thrun, S · 2017
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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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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Sun, C., Shrivastava, A., Singh, S., and Gupta, A · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Cited alongside, same era.
Exploring the limits of weakly supervised pretraining
Mahajan, D., Girshick, R. B., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., and van der Maaten, L · 2018
Cited alongside, same era.
Predictive uncertainty estimation via prior networks
Malinin, A. and Gales, M. J. F · 2018
Cited alongside, same era.
Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2019
Temporal probability calibration
Leathart, T. and Polaczuk, M · 2020
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Uncertainty quantification and deep ensembles
Rahaman, R. and Thiery, A. H · 2020
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Mitigating bias in calibration error estimation
Roelofs, R., Cain, N., Shlens, J., and Mozer, M. C · 2020
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Metnet: A neural weather model for precipitation forecasting
Sønderby, C. K., Espeholt, L., Heek, J., Dehghani, M., Oliver, A., Salimans, T., Agrawal, S., Hickey, J., and Kalchbrenner, N · 2020
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Measuring robustness to natural distribution shifts in image classification
Taori, R., Dave, A., Shankar, V., Carlini, N., Recht, B., and Schmidt, L · 2020
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Batchensemble: an alternative approach to efficient ensemble and lifelong learning
Wen, Y., Tran, D., and Ba, J · 2020
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
Cited alongside, same era.
Verified uncertainty calibration
Kumar, A., Liang, P. S., and Ma, T · 2019
Cited alongside, same era.
When does label smoothing help?
Müller, R., Kornblith, S., and Hinton, G. E · 2019
Cited alongside, same era.
Measuring calibration in deep learning
Nixon, J., Dusenberry, M. W., Zhang, L., Jerfel, G., and Tran, D · 2019
Cited alongside, same era.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Ovadia, Y., Fertig, E., Lakshminarayanan, B., Nowozin, S., Sculley, D., Dillon, J. V., Ren, J., Nado, Z., and Snoek, J · 2019
Cited alongside, same era.
Do imagenet classifiers generalize to imagenet?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2019
Cited alongside, same era.
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Hyperparameter ensembles for robustness and uncertainty quantification
Wenzel, F., Snoek, J., Tran, D., and Jenatton, R · 2020
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Self-training with noisy student improves imagenet classification
Xie, Q., Luong, M.-T., Hovy, E., and Le, Q. V · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
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Calibration of neural networks using splines
Gupta, K., Rahimi, A., Ajanthan, T., Mensink, T., Sminchisescu, C., and Hartley, R · 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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Natural adversarial examples
Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., and Song, D · 2021
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Second opinion needed: communicating uncertainty in medical machine learning
Kompa, B., Snoek, J., and Beam, A. L · 2021
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Supervised transfer learning at scale for medical imaging
Mustafa, B., Loh, A., Freyberg, J., MacWilliams, P., Wilson, M., McKinney, S. M., Sieniek, M., Winkens, J., Liu, Y., Bui, P., Prabhakara, S., Telang, U., Karthikesalingam, A., Houlsby, N., and Natarajan, V · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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MLP-Mixer: An all-MLP architecture for vision
Tolstikhin, I., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., Lucic, M., and Dosovitskiy, A · 2021
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Combining ensembles and data augmentation can harm your calibration
Wen, Y., Jerfel, G., Muller, R., Dusenberry, M. W., Snoek, J., Lakshminarayanan, B., and Tran, D · 2021
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