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The application of machine learning in safety-critical systems requires a reliable assessment of uncertainty.
Automated flower classification over a large number of classes
M.-E. Nilsback and A. Zisserman · 2008
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80 million tiny images: A large data set for nonparametric object and scene recognition
A. Torralba, R. Fergus, and W. T. Freeman · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
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Fine-grained visual classification of aircraft
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ImageNet Large Scale Visual Recognition Challenge
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
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Maximum resilience of artificial neural networks
C.-H. Cheng, G. Nührenberg, and H. Ruess · 2017
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On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Weinberger · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
D. Hendrycks and K. Gimpel · 2017
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Reluplex: An efficient smt solver for verifying deep neural networks
G. Katz, C. Barrett, D. Dill, K. Julian, and M. Kochenderfer · 2017
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Practical black-box attacks against machine learning
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami · 2017
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Places: A 10 million image database for scene recognition
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba · 2017
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Understanding deep neural networks with rectified linear unit
R. Arora, A. Basuy, P. Mianjyz, and A. Mukherjee · 2018
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
A. Athalye, N. Carlini, and D. Wagner · 2018
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On the effectiveness of interval bound propagation for training verifiably robust models
S. Gowal, K. Dvijotham, R. Stanforth, R. Bunel, C. Qin, J. Uesato, R. Arandjelovic, T. Mann, and P. Kohli · 2018
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Training confidence-calibrated classifiers for detecting out-of-distribution samples
K. Lee, H. Lee, K. Lee, and J. Shin · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
K. Lee, K. Lee, H. Lee, and J. Shin · 2018
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Enhancing the reliability of out-of-distribution image detection in neural networks
S. Liang, Y. Li, and R. Srikant · 2018
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
F. Croce and M. Hein · 2020
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Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
S. Dathathri, K. Dvijotham, A. Kurakin, A. Raghunathan, J. Uesato, R. Bunel, S. Shankar, J. Steinhardt, I. Goodfellow, P. Liang, et al · 2020
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Evaluating robustness of predictive uncertainty estimation: Are dirichlet-based models reliable?
A.-K. Kopetzki, B. Charpentier, D. Zügner, S. Giri, and S. Günnemann · 2020
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Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
A. Kristiadi, M. Hein, and P. Hennig · 2020
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Fixing asymptotic uncertainty of bayesian neural networks with infinite relu features
A. Kristiadi, M. Hein, and P. Hennig · 2020
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Valdu · 2018
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Predictive uncertainty estimation via prior networks
A. Malinin and M. Gales · 2018
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Differentiable abstract interpretation for provably robust neural networks
M. Mirman, T. Gehr, and M. Vechev · 2018
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Evidential deep learning to quantify classification uncertainty
M. Sensoy, L. Kaplan, and M. Kandemir · 2018
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Robustness may be at odds with accuracy
D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry · 2018
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Autoaugment: Learning augmentation strategies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2019
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Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem
M. Hein, M. Andriushchenko, and J. Bitterwolf · 2019
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The open images dataset v4
A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, A. Kolesnikov, et al · 2020
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Energy-based out-of-distribution detection
W. Liu, X. Wang, J. Owens, and Y. Li · 2020
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Towards neural networks that provably know when they don’t know
A. Meinke and M. Hein · 2020
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Towards stable and efficient training of verifiably robust neural networks
H. Zhang, H. Chen, C. Xiao, S. Gowal, R. Stanforth, B. Li, D. Boning, and C.-J. Hsieh · 2020
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Make sure you’re unsure: A framework for verifying probabilistic specifications
L. Berrada, S. Dathathri, K. Dvijotham, R. Stanforth, R. Bunel, J. Uesato, S. Gowal, M. P. Kumar, et al · 2021
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Large image datasets: A pyrrhic win for computer vision?
A. Birhane and V. U. Prabhu · 2021
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Atom: Robustifying out-of-distribution detection using outlier mining
J. Chen, Y. Li, X. Wu, Y. Liang, and S. Jha · 2021
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Robustbench: a standardized adversarial robustness benchmark
F. Croce, M. Andriushchenko, V. Sehwag, E. Debenedetti, N. Flammarion, M. Chiang, P. Mittal, and M. Hein · 2021
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Improving robustness using generated data
S. Gowal, S.-A. Rebuffi, O. Wiles, F. Stimberg, D. A. Calian, and T. A. Mann · 2021
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Certified defenses: Why tighter relaxations may hurt training?
N. Jovanović, M. Balunović, M. Baader, and M. Vechev · 2021
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D. Macêdo and T. Ludermir · 2021
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Entropic out-of-distribution detection: Seamless detection of unknown examples
D. Macêdo, T. I. Ren, C. Zanchettin, A. L. Oliveira, and T. Ludermir · 2021
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Outlier exposure with confidence control for out-of-distribution detection
A.-A. Papadopoulos, M. R. Rajati, N. Shaikh, and J. Wang · 2021
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Breaking down out-of-distribution detection: Many methods based on OOD training data estimate a combination of the same core quantities
J. Bitterwolf, A. Meinke, M. Augustin, and M. Hein · 2022
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