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Deep learning (DL) techniques have achieved great success in predictive accuracy in a variety of tasks, but deep neural networks (DNNs) are shown to produce highly overconfident scores for even abnormal samples.
G. W. Brier, “Verification of forecasts expressed in terms of probability,” Monthly weather review , vol. 78, no. 1, pp. 1–3, 1950
1950
Earlier work this paper cites.
R. Battiti and F. Masulli, “Bfgs optimization for faster and automated supervised learning,” in International neural network conference . Springer, 1990, pp. 757–760
1990
Earlier work this paper cites.
D. J. MacKay, “Information-based objective functions for active data selection,” Neural computation , vol. 4, no. 4, pp. 590–604, 1992
1992
Earlier work this paper cites.
——, “Bayesian interpolation,” Neural computation , vol. 4, no. 3, pp. 415–447, 1992
1992
Earlier work this paper cites.
——, “A practical bayesian framework for backpropagation networks,” Neural computation , vol. 4, no. 3, pp. 448–472, 1992
1992
Earlier work this paper cites.
C. Manning and H. Schutze, Foundations of statistical natural language processing . MIT press, 1999
1999
Earlier work this paper cites.
E. G. Barrantes, D. H. Ackley, S. Forrest, T. S. Palmer, D. Stefanovic, and D. D. Zovi, “Randomized instruction set emulation to disrupt binary code injection attacks,” in Proceedings of the 10th ACM conference on Computer and communications security , 2003, pp. 281–289
2003
Earlier work this paper cites.
J. Davis and M. Goadrich, “The relationship between precision-recall and roc curves,” in Proceedings of the 23rd international conference on Machine learning , 2006, pp. 233–240
2006
Earlier work this paper cites.
T. Fawcett, “An introduction to roc analysis,” Pattern recognition letters , vol. 27, no. 8, pp. 861–874, 2006
2006
Earlier work this paper cites.
T. Mens, “Introduction and roadmap: History and challenges of software evolution,” in Software evolution . Springer, 2008, pp. 1–11
2008
Earlier work this paper cites.
L. Pulina and A. Tacchella, “An abstraction-refinement approach to verification of artificial neural networks,” in International Conference on Computer Aided Verification . Springer, 2010, pp. 243–257
2010
Earlier work this paper cites.
J. Gama, I. Žliobaitė, A. Bifet, M. Pechenizkiy, and A. Bouchachia, “A survey on concept drift adaptation,” ACM computing surveys (CSUR) , vol. 46, no. 4, pp. 1–37, 2014
2014
Earlier work this paper cites.
X. Rong, “word2vec parameter learning explained,” arXiv preprint arXiv:1411.2738 , 2014
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , vol. 521, no. 7553, pp. 436–444, 2015
2015
Earlier work this paper cites.
J. M. Hernández-Lobato and R. Adams, “Probabilistic backpropagation for scalable learning of bayesian neural networks,” in International Conference on Machine Learning , 2015, pp. 1861–1869
2015
Earlier work this paper cites.
T. Saito and M. Rehmsmeier, “The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets,” PloS one , vol. 10, no. 3, p. e0118432, 2015
2015
Earlier work this paper cites.
2015
Cited alongside, same era.
D. P. Kingma, T. Salimans, and M. Welling, “Variational dropout and the local reparameterization trick,” in Advances in neural information processing systems , 2015, pp. 2575–2583
2015
Cited alongside, same era.
Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in international conference on machine learning , 2016, pp. 1050–1059
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Y. Tian, K. Pei, S. Jana, and B. Ray, “Deeptest: Automated testing of deep-neural-network-driven autonomous cars,” in Proceedings of the 40th international conference on software engineering , 2018, pp. 303–314
2018
Later among the works it cites.
M. Abuhamad, T. AbuHmed, A. Mohaisen, and D. Nyang, “Large-scale and language-oblivious code authorship identification,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security , 2018, pp. 101–114
2018
Later among the works it cites.
X. Gu, H. Zhang, and S. Kim, “Deep code search,” in 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE) . IEEE, 2018, pp. 933–944
2018
Later among the works it cites.
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” in 2016 IEEE European symposium on security and privacy (EuroS&P) . IEEE, 2016, pp. 372–387
2016
Cited alongside, same era.
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2574–2582
2016
Cited alongside, same era.
2016
Cited alongside, same era.
N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in 2017 ieee symposium on security and privacy (sp) . IEEE, 2017, pp. 39–57
2017
Cited alongside, same era.
2017
Cited alongside, same era.
J. Wang, G. Dong, J. Sun, X. Wang, and P. Zhang, “Adversarial sample detection for deep neural network through model mutation testing,” in 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, 2019, pp. 1245–1256
2019
Later among the works it cites.
J. Ren, P. J. Liu, E. Fertig, J. Snoek, R. Poplin, M. Depristo, J. Dillon, and B. Lakshminarayanan, “Likelihood ratios for out-of-distribution detection,” in Advances in Neural Information Processing Systems , 2019, pp. 14 707–14 718
2019
Later among the works it cites.
W. J. Maddox, P. Izmailov, T. Garipov, D. P. Vetrov, and A. G. Wilson, “A simple baseline for bayesian uncertainty in deep learning,” in Advances in Neural Information Processing Systems , 2019, pp. 13 153–13 164
2019
Later among the works it cites.
U. Alon, M. Zilberstein, O. Levy, and E. Yahav, “code2vec: Learning distributed representations of code,” Proceedings of the ACM on Programming Languages , vol. 3, no. POPL, pp. 1–29, 2019
2019
Later among the works it cites.
Y. Xiao and W. Y. Wang, “Quantifying uncertainties in natural language processing tasks,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 7322–7329
2019
Later among the works it cites.
J. Behrmann, W. Grathwohl, R. T. Chen, D. Duvenaud, and J.-H. Jacobsen, “Invertible residual networks,” in International Conference on Machine Learning , 2019, pp. 573–582
2019
Later among the works it cites.
2019
Later among the works it cites.
H. J. Kang, T. F. Bissyandé, and D. Lo, “Assessing the generalizability of code2vec token embeddings,” in 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2019, pp. 1–12
2019
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
H. Wang, J. Xu, C. Xu, X. Ma, and J. Lu, “Dissector: input validation for deep learning applications by crossing-layer dissection,” in Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering , 2020, pp. 727–738
2020
Later among the works it cites.
M. Allamanis, H. Peng, and C. Sutton, “A convolutional attention network for extreme summarization of source code,” in International conference on machine learning . PMLR, 2016, pp. 2091–2100
2091
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