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-[[start|{{home.png}}]][[start|Main]] [[install|{{tool.png}}]][[install|How to Install]]
+[[start|{{home.png}}]][[start|Main]] [[install|{{tool.png}}]][[install|Download]]  [[demonstration|{{imac.png}}]][[demonstration|Video Demos]]
 ======PRODeep======
 [[https://github.com/ISCAS-PMC/PRODeep|PRODeep]] is a platform for robustness verification of deep neural networks (DNNs). It incorporates constraint-based, abstraction-based, and optimisation-based robustness verification algorithms. It has a modular architecture, enabling easy comparison of different algorithms.
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+----
+=====Publications=====
+  * [[https://iscasmc.ios.ac.cn/iscasmcwp/?page_id=65&key=LiLYCHZ19|Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification]], Li, J.; Liu, J.; Yang, P.; Chen, L.; Huang, X. and Zhang, L. In SAS, pages 296-319, LNCS 11822, 2019.
+  * [[https://dl.acm.org/doi/10.1145/3368089.3417918|PRODeep: a platform for robustness verification of deep neural networks.]] Renjue Li, Jianlin Li, Cheng-Chao Huang, Pengfei Yang, Xiaowei Huang, Lijun Zhang, Bai Xue, and Holger Hermanns. In Proceedings of the ESEC/FSE 2020.
+  * [[https://arxiv.org/abs/2010.07722|Improving Neural Network Verification through Spurious Region Guided Refinement.]] Yang, P., Li, R., Li, J., Huang, C. C., Wang, J., Sun, J., Xue, B., Zhang, L. (2020). arXiv preprint arXiv:2010.07722.
+=====External Publications=====
+  * [[https://dl.acm.org/doi/10.1145/3290354|An abstract domain for certifying neural networks.]] Gagandeep Singh, Timon Gehr, Markus Püschel, and Martin Vechev. 2019. Proc. ACM Program. Lang. 3, POPL, Article 41 (January 2019), 30 pages.
+  * [[https://www.sri.inf.ethz.ch/publications/balunovic2019geometric|Certifying geometric robustness of neural networks.]] Balunovic, M., Baader, M., Singh, G., Gehr, T., & Vechev, M. (2019). In Advances in Neural Information Processing Systems (pp. 15313-15323).
+  * [[https://link.springer.com/chapter/10.1007/978-3-319-68167-2_19|Formal verification of piece-wise linear feed-forward neural networks.]] Ehlers, Ruediger. International Symposium on Automated Technology for Verification and Analysis. Springer, Cham, 2017.
+  * [[https://link.springer.com/chapter/10.1007/978-3-319-63387-9_5|Reluplex: An efficient SMT solver for verifying deep neural networks.]] Katz, G., Barrett, C., Dill, D. L., Julian, K., & Kochenderfer, M. J. (2017, July). In International Conference on Computer Aided Verification (pp. 97-117). Springer, Cham.