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Recurrent neural networks (RNNs) have been applied to a broad range of applications, including natural language processing, drug discovery, and video recognition.
Partition testing does not inspire confidence
Richard G. Hamlet and Ross Taylor · 1990
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Phyllis G. Frankl, Richard G. Hamlet, Bev Littlewood, and Lorenzo Strigini · 1998
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Finding motifs in time series
Jessica Lin, Eamonn Keogh, Stefano Lonardi, and Pranav Patel · 2002
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Diversity regularized machine
Yang Yu, Yu-Feng Li, and Zhi-Hua Zhou · 2011
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Testing for Continuous Delivery with Visual Studio 2012
Larry Brader, Howie Hilliker, and Alan Cameron Wills · 2012
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Ucf101: A dataset of 101 human action classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
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http://www.rdkit.org
RDKit: Open-source cheminformatics · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Crafting adversarial input sequences for recurrent neural networks
Nicolas Papernot, Patrick McDaniel, Ananthram Swami, and Richard Harang · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Deriving a frequentist conservative confidence bound for probability of failure per demand for systems with different operational and test profiles
Peter Bishop and Andrey Povyakalo · 2017
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Understanding hidden memories of recurrent neural networks
Yao Ming, Shaozu Cao, Ruixiang Zhang, Zhen Li, Yuanzhe Chen, Yangqiu Song, and Huamin Qu · 2017
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Deepxplore: Automated whitebox testing of deep learning systems
Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana · 2017
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Towards practical verification of machine learning: The case of computer vision systems
Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana · 2017
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Visualizing the hidden activity of artificial neural networks
Paulo E Rauber, Samuel G Fadel, Alexandre X Falcao, and Alexandru C Telea · 2017
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Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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Generating natural language adversarial examples
Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang · 2018
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Towards dependability metrics for neural networks
Chih-Hong Cheng, Georg Nührenberg, Chung-Hao Huang, Harald Ruess, and Hirotoshi Yasuoka · 2018
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Model checking
Edmund M Clarke Jr, Orna Grumberg, Daniel Kroening, Doron Peled, and Helmut Veith · 2018
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Crafting adversarial examples for speech paralinguistics applications
Yuan Gong and Christian Poellabauer · 2018
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Symbolic execution for deep neural networks
Divya Gopinath, Kaiyuan Wang, Mengshi Zhang, Corina S Pasareanu, and Sarfraz Khurshid · 2018
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Dlfuzz: Differential fuzzing testing of deep learning systems
Jianmin Guo, Yu Jiang, Yue Zhao, Quan Chen, and Jiaguang Sun · 2018
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DeepGauge: Comprehensive and multi-granularity testing criteria for gauging the robustness of deep learning systems
Lei Ma, Felix Juefei-Xu, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Chunyang Chen, Ting Su, Li Li, Yang Liu, Jianjun Zhao, and Yadong Wang · 2018
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DeepMutation: Mutation testing of deep learning systems
Lei Ma, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Felix Juefei-Xu, Chao Xie, Li Li, Yang Liu, Jianjun Zhao, et al · 2018
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Deepstellar: Model-based quantitative analysis of stateful deep learning systems
Xiaoning Du, Xiaofei Xie, Yi Li, Lei Ma, Yang Liu, and Jianjun Zhao · 2019
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Diversity in machine learning
Zhiqiang Gong, Ping Zhong, and Weidong Hu · 2019
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BadNets: Evaluating backdooring attacks on deep neural networks
Tianyu Gu, Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2019
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Rnn-test: Adversarial testing framework for recurrent neural network systems
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Guiding deep learning system testing using surprise adequacy
J. Kim, R. Feldt, and S. Yoo · 2019
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Structural coverage criteria for neural networks could be misleading
Zenan Li, Xiaoxing Ma, Chang Xu, and Chun Cao · 2019
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MODE: automated neural network model debugging via state differential analysis and input selection
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Poison frogs! targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
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Concolic testing for deep neural networks
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Detecting adversarial samples for deep neural networks through mutation testing
Jingyi Wang, Jun Sun, Peixin Zhang, and Xinyu Wang · 2018
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Feature-guided black-box safety testing of deep neural networks
Matthew Wicker, Xiaowei Huang, and Marta Kwiatkowska · 2018
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Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay Pande · 2018
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TensorFuzz: Debugging neural networks with coverage-guided fuzzing
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Global robustness evaluation of deep neural networks with provable guarantees for the hamming distance
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Structural test coverage criteria for deep neural networks
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On the convergence and robustness of adversarial training
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Correlations between deep neural network model coverage criteria and model quality
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Assessing safety-critical systems from operational testing: A study on autonomous vehicles
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