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Automated Technology for Verification and Analysis
DeepSafe: A Data-Driven Approach for Assessing Robustness of Neural Networks
other
Author(s):
Divya Gopinath
,
Guy Katz
,
Corina S. Păsăreanu
,
Clark Barrett
Publication date
(Online):
September 30 2018
Publisher:
Springer International Publishing
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Collective Dynamics
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Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini
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David Wagner
(2017)
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An efficient k-means clustering algorithm: analysis and implementation
T. Kanungo
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D.M. Mount
,
N.S. Netanyahu
…
(2002)
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The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot
,
Patrick McDaniel
,
Somesh Jha
…
(2016)
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Book Chapter
Publication date (Print):
2018
Publication date (Online):
September 30 2018
Pages
: 3-19
DOI:
10.1007/978-3-030-01090-4_1
SO-VID:
d67d203a-b51b-4f2a-8982-181af1f2330e
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Book chapters
pp. 3
DeepSafe: A Data-Driven Approach for Assessing Robustness of Neural Networks
pp. 194
Bisimilarity Distances for Approximate Differential Privacy
pp. 476
Ranking and Repulsing Supermartingales for Reachability in Probabilistic Programs
pp. 513
EthIR: A Framework for High-Level Analysis of Ethereum Bytecode
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