Fetching the paper…
Reading the bibliography…
Deep neural networks (DNN) are increasingly applied in safety-critical systems, e.g., for face recognition, autonomous car control and malware detection.
"general intelligence," objectively determined and measured
Charles Spearman · 1904
Earlier work this paper cites.
Notes on the history of correlation
KARL PEARSON · 1920
Earlier work this paper cites.
A new measure of rank correlation
Maurice G Kendall · 1938
Earlier work this paper cites.
Fundamental statistics in psychology and education
J B. Stroud · 1951
Earlier work this paper cites.
The mnist database of handwritten digits
Yann LeCun · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Differential testing for software
William M. McKeeman · 1998
Earlier work this paper cites.
A practical tutorial on modified condition/decision coverage
Kelly Hayhurst, Dan Veerhusen, John Chilenski, and Leanna Rierson · 2001
Earlier work this paper cites.
Image quality assessment: From error visibility to structural similarity
Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli · 2004
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Robustness and generalization
Huan Xu and Shie Mannor · 2012
Earlier work this paper cites.
Using frankencerts for automated adversarial testing of certificate validation in SSL/TLS implementations
Chad Brubaker, Suman Jana, Baishakhi Ray, Sarfraz Khurshid, and Vitaly Shmatikov · 2014
Earlier work this paper cites.
Coverage is not strongly correlated with test suite effectiveness
Laura Inozemtseva and Reid Holmes · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Droid-sec: Deep learning in android malware detection
Zhenlong Yuan, Yongqiang Lu, Zhaoguo Wang, and Yibo Xue · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
Earlier work this paper cites.
Very deep coanvolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Cited alongside, same era.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Cited alongside, same era.
End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Deepgauge: Multi-granularity testing criteria for deep learning systems
Lei Ma, Felix Juefei-Xu, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Chunyang Chen, Ting Su, Li Li, Yang Liu, et al · 2018
Later among the works it cites.
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
Later among the works it cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Later among the works it cites.
Tensorfuzz: Debugging neural networks with coverage-guided fuzzing
Augustus Odena and Ian Goodfellow · 2018
Later among the works it cites.
Youcheng Sun, Xiaowei Huang, and Daniel Kroening · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Nicolas Papernot, Fartash Faghri, Nicholas Carlini, Ian Goodfellow, Reuben Feinman, Alexey Kurakin, Cihang Xie, Yash Sharma, Tom Brown, and Aurko Roy · 2016
Cited alongside, same era.
The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami · 2016
Cited alongside, same era.
The asa’s statement on p-values: Context, process, and purpose
Ronald L Wasserstein, Nicole A Lazar, et al · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David A. Wagner · 2017
Cited alongside, same era.
Parseval networks: Improving robustness to adversarial examples
Moustapha Ciss, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier · 2017
Cited alongside, same era.
Formal guarantees on the robustness of a classifier against adversarial manipulation
Matthias Hein and Maksym Andriushchenko · 2017
Cited alongside, same era.
Reluplex: An efficient smt solver for verifying deep neural networks
Guy Katz, Clark Barrett, David L Dill, Kyle Julian, and Mykel J Kochenderfer · 2017
Cited alongside, same era.
Concolic testing for deep neural networks
Youcheng Sun, Min Wu, Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska, and Daniel Kroening · 2018
Later among the works it cites.
Deeptest: Automated testing of deep-neural-network-driven autonomous cars
Yuchi Tian, Kexin Pei, Suman Jana, and Baishakhi Ray · 2018
Later among the works it cites.
Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2018
Later among the works it cites.
Lipschitz regularity of deep neural networks: analysis and efficient estimation
Aladin Virmaux and Kevin Scaman · 2018
Later among the works it cites.
Evaluating the robustness of neural networks: An extreme value theory approach
Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, and Luca Daniel · 2018
Later among the works it cites.
Adversarial examples: Opportunities and challenges
Jiliang Zhang and Xiaoxiong Jiang · 2018
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Closest in time.
Efficient and accurate estimation of lipschitz constants for deep neural networks
Mahyar Fazlyab, Alexander Robey, Hamed Hassani, Manfred Morari, and George J Pappas · 2019
Closest in time.
Guiding deep learning system testing using surprise adequacy
Jinhan Kim, Robert Feldt, and Shin Yoo · 2019
Closest in time.
Structural coverage criteria for neural networks could be misleading
Zenan Li, Xiaoxing Ma, Chang Xu, and Chun Cao · 2019
Closest in time.
Deepsec: A uniform platform for security analysis of deep learning model
Xiang Ling, Shouling Ji, Jiaxu Zou, Jiannan Wang, Chunming Wu, Bo Li, and Ting Wang · 2019
Closest in time.
Adversarial sample detection for deep neural network through model mutation testing
Jingyi Wang, Guoliang Dong, Jun Sun, Xinyu Wang, and Peixin Zhang · 2019
Closest in time.