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The point estimates of ReLU classification networks---arguably the most widely used neural network architecture---have been shown to yield arbitrarily high confidence far away from the training data.
Verification of forecasts expressed in terms of probability
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Priors for infinite networks
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Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
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Optimizing neural networks with kronecker-factored approximate curvature
Martens, J. and Grosse, R · 2015
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Obtaining well calibrated probabilities using bayesian binning
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., and Clune, J · 2015
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Scalable bayesian optimization using deep neural networks
Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M., Prabhat, M., and Adams, R · 2015
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Concrete problems in ai safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D · 2016
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Uncertainty in deep learning
Gal, Y · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Structured and efficient variational deep learning with matrix gaussian posteriors
Louizos, C. and Welling, M · 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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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
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Evidential deep learning to quantify classification uncertainty
Sensoy, M., Kaplan, L., and Kandemir, M · 2018
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Noisy natural gradient as variational inference
Zhang, G., Sun, S., Duvenaud, D., and Grosse, R · 2018
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TRADI: Tracking deep neural network weight distributions
Franchi, G., Bursuc, A., Aldea, E., Dubuisson, S., and Bloch, I · 2019
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Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem
Hein, M., Andriushchenko, M., and Bitterwolf, J · 2019
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Deep anomaly detection with outlier exposure
Hendrycks, D., Mazeika, M., and Dietterich, T · 2019
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Adv-BNN: Improved adversarial defense through robust bayesian neural network
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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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Multiplicative normalizing flows for variational Bayesian neural networks
Louizos, C. and Welling, M · 2017
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Understanding deep neural networks with rectified linear units
Arora, R., Basu, A., Mianjy, P., and Mukherjee, A · 2018
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GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Gardner, J. R., Pleiss, G., Bindel, D., Weinberger, K. Q., and Wilson, A. G · 2018
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Lightweight probabilistic deep networks
Gast, J. and Roth, S · 2018
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Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S., Li, Y., and Srikant, R · 2018
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Liu, X., Li, Y., Wu, C., and Hsieh, C.-J · 2019
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A simple baseline for bayesian uncertainty in deep learning
Maddox, W. J., Izmailov, P., Garipov, T., Vetrov, D. P., and Wilson, A. G · 2019
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Reverse kl-divergence training of prior networks: Improved uncertainty and adversarial robustness
Malinin, A. and Gales, M · 2019
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Benchmarking the neural linear model for regression
Ober, S. W. and Rasmussen, C. E · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
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Non-parametric calibration for classification
Wenger, J., Kjellström, H., and Triebel, R · 2019
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Deterministic variational inference for robust bayesian neural networks
Wu, A., Nowozin, S., Meeds, E., Turner, R. E., Hernandez-Lobato, J. M., and Gaunt, A. L · 2019
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On last-layer algorithms for classification: Decoupling representation from uncertainty estimation
Brosse, N., Riquelme, C., Martin, A., Gelly, S., and Moulines, É · 2020
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BackPACK: Packing more into Backprop
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Lu, Z., Ie, E., and Sha, F · 2020
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Towards neural networks that provably know when they don’t know
Meinke, A. and Hein, M · 2020
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