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Dirichlet-based uncertainty (DBU) models are a recent and promising class of uncertainty-aware models.
The relationship between precision-recall and roc curves
Davis, J. and Goadrich, M · 2006
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Cifar-10
Krizhevsky, A., Nair, V., and Hinton, G · 2009
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MNIST handwritten digit database
LeCun, Y. and Cortes, C · 2010
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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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
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Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
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Towards deep neural network architectures robust to adversarial examples
Gu, S. and Rigazio, L · 2015
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The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets
Saito, T. and Rehmsmeier, M · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
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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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Improving the robustness of deep neural networks via stability training
Zheng, S., Song, Y., Leung, T., and Goodfellow, I · 2016
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Adversarial examples are not easily detected: Bypassing ten detection methods
Carlini, N. and Wagner, D · 2017
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
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Parseval networks: Improving robustness to adversarial examples
Cisse, M., Bojanowski, P., Grave, E., Dauphin, Y., and Usunier, N · 2017
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UCI machine learning repository
Dua, D. and Graff, C · 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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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Brendel, W., Rauber, J., and Bethge, M · 2018
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Deep learning for classical japanese literature
Clanuwat, T., Bober-Irizar, M., Kitamoto, A., Lamb, A., Yamamoto, K., and Ha, D · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Lee, K., Lee, K., Lee, H., and Shin, J · 2018
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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Evidential deep learning to quantify classification uncertainty
Sensoy, M., Kaplan, L., and Kandemir, M · 2018
Cited alongside, same era.
Understanding measures of uncertainty for adversarial example detection
Smith, L. and Gal, Y · 2018
Cited alongside, same era.
Evaluating the robustness of neural networks: An extreme value theory approach
Weng, T.-W., Zhang, H., Chen, P.-Y., Yi, J., Su, D., Gao, Y., Hsieh, C.-J., and Daniel, L · 2018
Provable worst case guarantees for the detection of out-of-distribution data
Bitterwolf, J., Meinke, A., and Hein, M · 2020
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Efficient robustness certificates for discrete data: Sparsity-aware randomized smoothing for graphs, images and more
Bojchevski, A., Klicpera, J., and Günnemann, S · 2020
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Robustness of bayesian neural networks to gradient-based attacks
Carbone, G., Wicker, M., Laurenti, L., Patane, A., Bortolussi, L., and Sanguinetti, G · 2020
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Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts
Charpentier, B., Zügner, D., and Günnemann, S · 2020
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Seq2sick: Evaluating the robustness of sequence-to-sequence models with adversarial examples
Cheng, M., Yi, J., Chen, P.-Y., Zhang, H., and Hsieh, C.-J · 2020
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Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Wong, E. and Kolter, J. Z · 2018
Cited alongside, same era.
Adversarial attacks on neural networks for graph data
Zügner, D., Akbarnejad, A., and Günnemann, S · 2018
Cited alongside, same era.
Certifiable robustness to graph perturbations
Bojchevski, A. and Günnemann, S · 2019
Cited alongside, same era.
Statistical guarantees for the robustness of bayesian neural networks
Cardelli, L., Kwiatkowska, M., Laurenti, L., Paoletti, N., Patane, A., and Wicker, M · 2019
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Cohen, J. M., Rosenfeld, E., and Kolter, J. Z · 2019
Cited alongside, same era.
A simple baseline for bayesian uncertainty in deep learning
Maddox, W. J., Izmailov, P., Garipov, T., Vetrov, D. P., and Wilson, A. G · 2019
Cited alongside, same era.
Dang-Nhu, R., Singh, G., Bielik, P., and Vechev, M · 2020
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Certifying confidence via randomized smoothing
Kumar, A., Levine, A., Feizi, S., and Goldstein, T · 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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Towards maximizing the representation gap between in-domain & out-of-distribution examples
Nandy, J., Hsu, W., and Lee, M · 2020
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Improving uncertainty estimates through the relationship with adversarial robustness
Qin, Y., Wang, X., Beutel, A., and Chi, E. H · 2020
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Uncertainty-aware deep classifiers using generative models
Sensoy, M., Kaplan, L., Cerutti, F., and Saleki, M · 2020
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Multifaceted uncertainty estimation for label-efficient deep learning
Shi, W., Zhao, X., Chen, F., and Yu, Q · 2020
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Confidence-calibrated adversarial training: Generalizing to unseen attacks
Stutz, D., Hein, M., and Schiele, B · 2020
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Probabilistic safety for bayesian neural networks
Wicker, M., Laurenti, L., Patane, A., and Kwiatkowska, M · 2020
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Detection as regression: Certified object detection by median smoothing
yeh Chiang, P., Curry, M. J., Abdelkader, A., Kumar, A., Dickerson, J., and Goldstein, T · 2020
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Uncertainty aware semi-supervised learning on graph data
Zhao, X., Chen, F., Hu, S., and Cho, J.-H · 2020
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Reachable sets of classifiers and regression models: (non-)robustness analysis and robust training
Kopetzki, A.-K. and Günnemann, S · 2021
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Collective robustness certificates: Exploiting interdependence in graph neural networks
Schuchardt, J., Bojchevski, A., Klicpera, J., and Günnemann, S · 2021
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