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More readings on verifying, synthesizing NN.

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- Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, and Martin Vechev
[*AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation*](https://files.sri.inf.ethz.ch/website/papers/sp2018.pdf).
S&P 2018.
- Matthew Mirman, Timon Gehr, and Martin Vechev.
[*Differentiable Abstract Interpretation for Provably Robust Neural Networks*](http://proceedings.mlr.press/v80/mirman18b/mirman18b.pdf).
ICML 2018.
- Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind.
[*Automatic differentiation in machine learning: a survey*](https://arxiv.org/pdf/1502.05767).
JMLR 2018.
- Gagandeep Singh, Timon Gehr, Markus Püschel, and Martin T. Vechev.
[*An Abstract Domain for Certifying Neural Networks*](https://files.sri.inf.ethz.ch/website/papers/DeepPoly.pdf).
POPL 2019.
- Marc Fischer, Mislav Balunovic, Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, and Martin Vechev.
[*DL2: Training and Querying Neural Networks with Logic*](http://proceedings.mlr.press/v97/fischer19a/fischer19a.pdf).
ICML 2019.
- Abhinav Verma, Hoang M. Le, Yisong Yue, and Swarat Chaudhuri.
[*Imitation-Projected Programmatic Reinforcement Learning*](https://arxiv.org/pdf/1907.05431).
NeurIPS 2019.
# Supplemental Material
- Cynthia Dwork and Aaron Roth.