FoMLAS

Accepted Papers

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1. An SMT-Based Approach for Verifying Binarized Neural Networks
   Authors: Guy Amir, Haoze Wu, Clark Barrett and Guy Katz
    

2. Certified Robustness to Programmable Transformations in LSTMs
   Authors: Yuhao Zhang, Aws Albarghouthi and Loris D’antoni
    

3. Logically Constrained Pruning Towards Robust Neural Networks
   Authors: Kirsty Duncan, Robert Stewart and Ekaterina Komendantskaya
    

4. Refutation-based Adversarial Robustness Verification of Deep Neural Networks
   Authors: Joshua Smith, Jarom Allen, Viswanathan Swaminathan and Zhen Zhang
    

5. Tightening Robustness Verification of Convolutional Neural Networks with Fine-Grained Linear Approximation
   Authors: Yiting Wu and Min Zhang
    

6. Scalable Synthesis of Verified Controllers in Deep Reinforcement Learning
   Authors: Zikang Xiong and Suresh Jagannathan
    

7. PEREGRiNN: Penalized-Relaxation Greedy Neural Network Verifier
   Authors: Haitham Khedr, James Ferlez and Yasser Shoukry
    

8. Provable Repair of Deep Neural Networks
   Authors: Aditya Thakur
    

9. NEUROSPF: A tool for the Symbolic Analysis of Neural Networks
   Authors: Muhammad Usman, Yannic Noller, Corina S. Pasareanu, Youcheng Sun and Divya Gopinath
    

10. NeVer2: A Framework for Learning and Verification of Neural Networks
    Authors: Dario Guidotti, Luca Pulina and Armando Tacchella