Fetching the paper…
Reading the bibliography…
Deep neural networks represent the state of the art in machine learning in a growing number of fields, including vision, speech and natural language processing.
A. Cohen, I. Daubechies, and J.-C. Feauveau, “Biorthogonal bases of compactly supported wavelets,” Communications on Pure and Applied Mathematics
1992
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE
1998
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” in International Conference on Learning Representations (ICLR)
2014
Earlier work this paper cites.
A. Makhzani and B. Frey, “ k k -Sparse autoencoders,” in International Conference on Learning Representations (ICLR)
2014
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in International Conference on Learning Representations (ICLR)
2015
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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Cited alongside, same era.
B. Poole, S. Lahiri, M. Raghu, J. Sohl-Dickstein, and S. Ganguli, “Exponential expressivity in deep neural networks through transient chaos,” in Advances in Neural Information Processing Systems (NIPS)
2016
Cited alongside, same era.
A. Fawzi, S.-M. Moosavi-Dezfooli, and P. Frossard, “The robustness of deep networks: A geometrical perspective,” IEEE Signal Processing Magazine
2017
Cited alongside, same era.
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…