| Weighted model counting with conditional weights for Bayesian networks P Dilkas, V Belle Uncertainty in Artificial Intelligence, 386-396, 2021 | 13 | 2021 |
| Synthesising Recursive Functions for First-Order Model Counting: Challenges, Progress, and Conjectures P Dilkas, V Belle arXiv preprint arXiv:2306.04189, 2023 | 8 | 2023 |
| Weighted model counting without parameter variables P Dilkas, V Belle Theory and Applications of Satisfiability Testing–SAT 2021: 24th …, 2021 | 8 | 2021 |
| Mapping the neuro-symbolic AI landscape by architectures: A handbook on augmenting deep learning through symbolic reasoning J Feldstein, P Dilkas, V Belle, E Tsamoura arXiv preprint arXiv:2410.22077, 2024 | 6 | 2024 |
| Generating random logic programs using constraint programming P Dilkas, V Belle Principles and Practice of Constraint Programming: 26th International …, 2020 | 6 | 2020 |
| Generating Random Instances of Weighted Model Counting: An Empirical Analysis with Varying Primal Treewidth P Dilkas International Conference on Integration of Constraint Programming …, 2023 | 3 | 2023 |
| Generalising weighted model counting P Dilkas The University of Edinburgh, 2023 | 2 | 2023 |
| Algorithm selection for maximum common subgraph P Dilkas Bachelor’s thesis, University of Glasgow, 2018 | 1 | 2018 |
| Towards Practical First-Order Model Counting AK Kidambi, G Singh, P Dilkas, KS Meel arXiv preprint arXiv:2502.12278, 2025 | | 2025 |
