| An abstraction-refinement approach to verification of artificial neural networks L Pulina, A Tacchella Computer Aided Verification: 22nd International Conference, CAV 2010 …, 2010 | 375 | 2010 |
| Challenging SMT solvers to verify neural networks L Pulina, A Tacchella Ai Communications 25 (2), 117-135, 2012 | 163 | 2012 |
| A self-adaptive multi-engine solver for quantified Boolean formulas L Pulina, A Tacchella Constraints 14, 80-116, 2009 | 126 | 2009 |
| Minimal Module Extraction from DL-Lite Ontologies Using QBF Solvers. R Kontchakov, L Pulina, U Sattler, T Schneider, P Selmer, F Wolter, ... IJCAI 9, 836-841, 2009 | 101 | 2009 |
| Automated verification of neural networks: Advances, challenges and perspectives F Leofante, N Narodytska, L Pulina, A Tacchella arXiv preprint arXiv:1805.09938, 2018 | 90 | 2018 |
| A multi-engine solver for quantified boolean formulas L Pulina, A Tacchella International Conference on Principles and Practice of Constraint …, 2007 | 88 | 2007 |
| A multi-engine approach to answer-set programming M Maratea, L Pulina, F Ricca Theory and Practice of Logic Programming 14 (6), 841-868, 2014 | 78 | 2014 |
| Collaborative expert portfolio management D Stern, H Samulowitz, R Herbrich, T Graepel, L Pulina, A Tacchella Proceedings of the AAAI Conference on Artificial Intelligence 24 (1), 179-184, 2010 | 70* | 2010 |
| A Survey on Applications of Quantified Boolean Formulas A Shukla, A Biere, M Seidl, L Pulina ICTAI 2019, 2019 | 60 | 2019 |
| Report of the third QBF solvers evaluation M Narizzano, L Pulina, A Tacchella Journal on Satisfiability, Boolean Modeling and Computation 2, 145-164, 2006 | 55 | 2006 |
| The seventh QBF solvers evaluation (QBFEVAL’10) C Peschiera, L Pulina, A Tacchella, U Bubeck, O Kullmann, I Lynce Theory and Applications of Satisfiability Testing–SAT 2010: 13th …, 2010 | 45 | 2010 |
| NeVer: a tool for artificial neural networks verification L Pulina, A Tacchella Annals of Mathematics and Artificial Intelligence 62, 403-425, 2011 | 43 | 2011 |
| Evaluating and certifying QBFs: A comparison of state-of-the-art tools M Narizzano, C Peschiera, L Pulina, A Tacchella AI communications 22 (4), 191-210, 2009 | 39 | 2009 |
| Temporal and Spatial OBDA with Many-Dimensional Halpern-Shoham Logic R Kontchakov, L Pandolfo, L Pulina, V Ryzhikov, M Zakharyaschev IJCAI, 2016 | 38 | 2016 |
| The 2016 and 2017 QBF solvers evaluations (QBFEVAL'16 and QBFEVAL'17) L Pulina, M Seidl Artificial Intelligence 274, 224-248, 2019 | 36 | 2019 |
| Verification of neural networks: Enhancing scalability through pruning D Guidotti, F Leofante, L Pulina, A Tacchella ECAI 2020, 2505-2512, 2020 | 34 | 2020 |
| Verification and repair of control policies for safe reinforcement learning S Pathak, L Pulina, A Tacchella Applied Intelligence 48, 886-908, 2018 | 34 | 2018 |
| Twelve years of QBF evaluations: QSAT is PSPACE-hard and it shows P Marin, M Narizzano, L Pulina, A Tacchella, E Giunchiglia Fundamenta Informaticae 149 (1-2), 133-158, 2016 | 28 | 2016 |
| pynever: A framework for learning and verification of neural networks D Guidotti, L Pulina, A Tacchella Automated Technology for Verification and Analysis: 19th International …, 2021 | 25 | 2021 |
| The Ninth QBF Solvers Evaluation–Preliminary Report L Pulina Proceedings of the 4th International Workshop on Quantified Boolean Formulas …, 2016 | 25 | 2016 |
