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Deep neural networks (DNNs) are vulnerable to maliciously generated adversarial examples.
Principles of Mathematical Analysis
W. Rudin et al · 1964
Earlier work this paper cites.
Methods for visual understanding of hierarchical system structures
K. Sugiyama, S. Tagawa, and M. Toda · 1981
Earlier work this paper cites.
Multielement visual tracking: Attention and perceptual organization
S. Yantis · 1992
Earlier work this paper cites.
Dot plots
L. Wilkinson · 1999
Earlier work this paper cites.
Numerical optimization
S. Wright and J. Nocedal · 1999
Earlier work this paper cites.
The spatial resolution of visual attention
J. Intriligator and P. Cavanagh · 2001
Earlier work this paper cites.
On spectral clustering: Analysis and an algorithm
A. Y. Ng, M. I. Jordan, and Y. Weiss · 2002
Earlier work this paper cites.
Opening the black box - data driven visualization of neural networks
F. Y. Tzeng and K. L. Ma · 2005
Earlier work this paper cites.
Pattern recognition and machine learning
C. M. Bishop · 2006
Earlier work this paper cites.
Visualizing set concordance with permutation matrices and fan diagrams
B. Kim, B. Lee, and J. Seo · 2007
Earlier work this paper cites.
Introduction to algorithms
T. H. Cormen · 2009
Earlier work this paper cites.
Untangling euler diagrams
N. H. Riche and T. Dwyer · 2010
Earlier work this paper cites.
Design study of linesets, a novel set visualization technique
B. Alper, N. Riche, G. Ramos, and M. Czerwinski · 2011
Earlier work this paper cites.
Human-centered approaches in geovisualization design: Investigating multiple methods through a long-term case study
D. Lloyd and J. Dykes · 2011
Earlier work this paper cites.
Context-preserving visual links
M. Steinberger, M. Waldner, M. Streit, A. Lex, and D. Schmalstieg · 2011
Earlier work this paper cites.
Representation learning: A review and new perspectives
Y. Bengio, A. Courville, and P. Vincent · 2013
Earlier work this paper cites.
Storyflow: Tracking the evolution of stories
S. Liu, Y. Wu, E. Wei, M. Liu, and Y. Liu · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
Visualizing sets and set-typed data: State-of-the-art and future challenges
B. Alsallakh, L. Micallef, W. Aigner, H. Hauser, S. Miksch, and P. Rodgers · 2014
Earlier work this paper cites.
Overview: The design, adoption, and analysis of a visual document mining tool for investigative journalists
M. Brehmer, S. Ingram, J. Stray, and T. Munzner · 2014
Earlier work this paper cites.
How hierarchical topics evolve in large text corpora
W. Cui, S. Liu, Z. Wu, and H. Wei · 2014
Earlier work this paper cites.
A survey on information visualization: recent advances and challenges
S. Liu, W. Cui, Y. Wu, and M. Liu · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Cited alongside, same era.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
Cited alongside, same era.
An interactive node-link visualization of convolutional neural networks
A. W. Harley · 2015
Cited alongside, same era.
Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
Cited alongside, same era.
Tensorflow
Google · 2017
Later among the works it cites.
Densely connected convolutional networks
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten · 2017
Later among the works it cites.
Towards better analysis of deep convolutional neural networks
M. Liu, J. Shi, Z. Li, C. Li, J. Zhu, and S. Liu · 2017
Later among the works it cites.
Understanding hidden memories of recurrent neural networks
Y. Ming, S. Cao, R. Zhang, Z. Li, Y. Chen, Y. Song, and H. Qu · 2017
Later among the works it cites.
Universal adversarial perturbations
S. M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2017
Later among the works it cites.
Towards robust detection of adversarial examples
T. Pang, C. Du, Y. Dong, and J. Zhu · 2017
Later among the works it cites.
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A. Nguyen, J. Yosinski, and J. Clune · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
Cited alongside, same era.
Object detectors emerge in deep scene CNNs
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2015
Cited alongside, same era.
Task-driven comparison of topic models
E. Alexander and M. Gleicher · 2016
Cited alongside, same era.
Deep learning
I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Visualizing the hidden activity of artificial neural networks
P. E. Rauber, S. G. Fadel, A. X. Falcao, and A. C. Telea · 2017
Later among the works it cites.
A. S. Ross and F. Doshi-Velez · 2017
Later among the works it cites.
Nips 2017: Adversarial attack
N. I. P. Systems · 2017
Later among the works it cites.
Threat of adversarial attacks on deep learning in computer vision: A survey
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Do convolutional neural networks learn class hierarchy?
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N. Pezzotti, T. Höllt, J. Van Gemert, B. P. Lelieveldt, E. Eisemann, and A. Vilanova · 2018
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N. Rodrigues and D. Weiskopf · 2018
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