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Strong theoretical guarantees of robustness can be given for ensembles of classifiers generated by input randomization.
Ix. on the problem of the most efficient tests of statistical hypotheses
J. Neyman and E. S. Pearson · 1933
Earlier work this paper cites.
The use of confidence or fiducial limits illustrated in the case of the binomial
C. J. Clopper and E. S. Pearson · 1934
Earlier work this paper cites.
Extension of the neyman-pearson theory of tests to discontinuous variates
K. Tocher · 1950
Earlier work this paper cites.
The properties of known drugs. 1. molecular frameworks
G. W. Bemis and M. A. Murcko · 1996
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Extended-connectivity fingerprints
D. Rogers and M. Hahn · 2010
Earlier work this paper cites.
Explaining and harnessing adversarial examples
I. Goodfellow, J. Shlens, and C. Szegedy · 2015
Earlier work this paper cites.
Towards verification of artificial neural networks
K. Scheibler, L. Winterer, R. Wimmer, and B. Becker · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Computational modeling of β \beta -secretase 1 (bace-1) inhibitors using ligand based approaches
G. Subramanian, B. Ramsundar, V. Pande, and R. A. Denny · 2016
Earlier work this paper cites.
Mitigating evasion attacks to deep neural networks via region-based classification
X. Cao and N. Z. Gong · 2017
Earlier work this paper cites.
Provably minimally-distorted adversarial examples
N. Carlini, G. Katz, C. Barrett, and D. L. Dill · 2017
Earlier work this paper cites.
Maximum resilience of artificial neural networks
C.-H. Cheng, G. Nührenberg, and H. Ruess · 2017
Earlier work this paper cites.
Formal verification of piece-wise linear feed-forward neural networks
R. Ehlers · 2017
Earlier work this paper cites.
Reluplex: An efficient smt solver for verifying deep neural networks
G. Katz, C. Barrett, D. L. Dill, K. Julian, and M. J. Kochenderfer · 2017
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An approach to reachability analysis for feed-forward relu neural networks
A. Lomuscio and L. Maganti · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Cited alongside, same era.
Evaluating robustness of neural networks with mixed integer programming
V. Tjeng, K. Xiao, and R. Tedrake · 2017
Cited alongside, same era.
Provable robustness of relu networks via maximization of linear regions
F. Croce, M. Andriushchenko, and M. Hein · 2018
Cited alongside, same era.
Output range analysis for deep feedforward neural networks
Certified defenses against adversarial examples
A. Raghunathan, J. Steinhardt, and P. Liang · 2018
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Semidefinite relaxations for certifying robustness to adversarial examples
A. Raghunathan, J. Steinhardt, and P. S. Liang · 2018
Later among the works it cites.
Fast and effective robustness certification
G. Singh, T. Gehr, M. Mirman, M. Püschel, and M. Vechev · 2018
Later among the works it cites.
Towards fast computation of certified robustness for relu networks
T.-W. Weng, H. Zhang, H. Chen, Z. Song, C.-J. Hsieh, D. Boning, I. S. Dhillon, and L. Daniel · 2018
Later among the works it cites.
Provable defenses against adversarial examples via the convex outer adversarial polytope
E. Wong and J. Z. Kolter · 2018
Later among the works it cites.
Scaling provable adversarial defenses
E. Wong, F. Schmidt, J. H. Metzen, and J. Z. Kolter · 2018
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S. Dutta, S. Jha, S. Sankaranarayanan, and A. Tiwari · 2018
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Training verified learners with learned verifiers
K. Dvijotham, S. Gowal, R. Stanforth, R. Arandjelovic, B. O’Donoghue, J. Uesato, and P. Kohli · 2018
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A dual approach to scalable verification of deep networks
K. Dvijotham, R. Stanforth, S. Gowal, T. Mann, and P. Kohli · 2018
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Deep neural networks and mixed integer linear optimization
M. Fischetti and J. Jo · 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, T. Mann, and P. Kohli · 2018
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Second-order adversarial attack and certifiable robustness
B. Li, C. Chen, W. Wang, and L. Carin · 2018
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Towards robust neural networks via random self-ensemble
X. Liu, M. Cheng, H. Zhang, and C.-J. Hsieh · 2018
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Moleculenet: a benchmark for molecular machine learning
Z. Wu, B. Ramsundar, E. N. Feinberg, J. Gomes, C. Geniesse, A. S. Pappu, K. Leswing, and V. Pande · 2018
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Efficient neural network robustness certification with general activation functions
H. Zhang, T.-W. Weng, P.-Y. Chen, C.-J. Hsieh, and L. Daniel · 2018
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Certified adversarial robustness via randomized smoothing
J. M. Cohen, E. Rosenfeld, and J. Z. Kolter · 2019
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The logbarrier adversarial attack: making effective use of decision boundary information
C. Finlay, A.-A. Pooladian, and A. M. Oberman · 2019
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Achieving verified robustness to symbol substitutions via interval bound propagation
P.-S. Huang, R. Stanforth, J. Welbl, C. Dyer, D. Yogatama, S. Gowal, K. Dvijotham, and P. Kohli · 2019
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Certified robustness to adversarial word substitutions
R. Jia, A. Raghunathan, K. Göksel, and P. Liang · 2019
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Certified robustness to adversarial examples with differential privacy
M. Lecuyer, V. Atlidakis, R. Geambasu, D. Hsu, and S. Jana · 2019
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Towards robust, locally linear deep networks
G.-H. Lee, D. Alvarez-Melis, and T. S. Jaakkola · 2019
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