Fetching the paper…
Reading the bibliography…
To use neural networks in safety-critical settings it is paramount to provide assurances on their runtime operation.
Convex optimization
Boyd, S. and Vandenberghe, L · 2004
Earlier work this paper cites.
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-r., and Hinton, G · 2013
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
Earlier work this paper cites.
Convolutional neural networks for sentence classification
Kim, Y · 2014
Earlier work this paper cites.
On the number of linear regions of deep neural networks
Montufar, G. F., Pascanu, R., Cho, K., and Bengio, Y · 2014
Earlier work this paper cites.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
Earlier work this paper cites.
End to end learning for self-driving cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al · 2016
Cited alongside, same era.
Safety verification of deep neural networks
Huang, X., Kwiatkowska, M., Wang, S., and Wu, M · 2016
Cited alongside, same era.
Formal verification of piece-wise linear feed-forward neural networks
Ehlers, R · 2017
Cited alongside, same era.
Reluplex: An efficient SMT solver for verifying deep neural networks
Katz, G., Barrett, C. W., Dill, D. L., Julian, K., and Kochenderfer, M. J · 2017
Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
On the expressive power of deep neural networks
Raghu, M., Poole, B., Kleinberg, J., Ganguli, S., and Dickstein, J. S · 2017
Later among the works it cites.
Bounding and counting linear regions of deep neural networks
Serra, T., Tjandraatmadja, C., and Ramalingam, S · 2017
Later among the works it cites.
Mastering the game of go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al · 2017
Later among the works it cites.
Verifying neural networks with mixed integer programming
Tjeng, V. and Tedrake, R · 2017
Later among the works it cites.
Certified defenses against adversarial examples
Raghunathan, A., Steinhardt, J., and Liang, P · 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…
Kolter, J. Z. and Wong, E · 2017
Cited alongside, same era.
An approach to reachability analysis for feed-forward relu neural networks
Lomuscio, A. and Maganti, L · 2017
Cited alongside, same era.
Output reachable set estimation and verification for multi-layer neural networks
Xiang, W., Tran, H., and Johnson, T. T
Cited in the paper.
Reachable set computation and safety verification for neural networks with relu activations
Xiang, W., Tran, H., and Johnson, T. T
Cited in the paper.
Formal security analysis of neural networks using symbolic intervals
Wang, S., Pei, K., Whitehouse, J., Yang, J., and Jana, S · 2018
Later among the works it cites.