| Rnn-surv: A deep recurrent model for survival analysis E Giunchiglia, A Nemchenko, M van der Schaar Artificial Neural Networks and Machine Learning–ICANN 2018: 27th …, 2018 | 115 | 2018 |
| Coherent Hierarchical Multi-Label Classification Networks E Giunchiglia, T Lukasiewicz Proceedings of the 34th Annual Conference on Neural Information Processing …, 2020 | 80 | 2020 |
| Can I trust the explainer? Verifying post-hoc explanatory methods. OM Camburu, E Giunchiglia, J Foerster, T Lukasiewicz, P Blunsom NeurIPS 2019 Workshop on Safety and Robustness in Decision Making, Vancouver …, 2019 | 62* | 2019 |
| Deep Learning with Logical Constraints E Giunchiglia, MC Stoian, T Lukasiewicz Proceedings of the 31st International Joint Conference on Artificial …, 2022 | 49 | 2022 |
| Engineering multi-agent systems: State of affairs and the road ahead V Mascardi, D Weyns, A Ricci, CB Earle, A Casals, M Challenger, ... ACM SIGSOFT Software Engineering Notes 44 (1), 18-28, 2019 | 46 | 2019 |
| Multi-Label Classification Neural Networks with Hard Logical Constraints E Giunchiglia, T Lukasiewicz Journal of Artificial Intelligence Research (JAIR) 72, 759-818, 2021 | 41 | 2021 |
| ROAD-R: The Autonomous Driving Dataset with Logical Requirements E Giunchiglia, MC Stoian, S Khan, F Cuzzolin, T Lukasiewicz Machine Learning, 2023 | 29* | 2023 |
| Conditional behavior trees: Definition, executability, and applications E Giunchiglia, M Colledanchise, L Natale, A Tacchella 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC …, 2019 | 19 | 2019 |
| The struggles of feature-based explanations: Shapley values vs. minimal sufficient subsets OM Camburu, E Giunchiglia, J Foerster, T Lukasiewicz, P Blunsom AAAI Explainable Agency in Artificial Intelligence Workshop 2021, 2020 | 18 | 2020 |
| Knowledge Graph Extraction from Videos L Mahon, E Giunchiglia, B Li, T Lukasiewicz 2020 IEEE International Conference on Machine Learning and Applications …, 2020 | 17 | 2020 |
| Lightweight visual question answering using scene graphs SV Nuthalapati, R Chandradevan, E Giunchiglia, B Li, M Kayser, ... Proceedings of the 30th ACM International Conference on Information …, 2021 | 12 | 2021 |
| Machine Learning with Requirements: a Manifesto E Giunchiglia, F Imrie, M van der Schaar, T Lukasiewicz arXiv preprint arXiv:2304.03674, 2023 | 5 | 2023 |
| Timotheus Kampik, Geylani Kardas, Vincent J V Mascardi, D Weyns, A Ricci, CB Earle, A Casals, M Challenger, ... Koeman, John Bruntse Larsen, Simon Mayer, Tasio Méndez, Juan Carlos Nieves …, 2019 | 4 | 2019 |
| Exploiting T-norms for Deep Learning in Autonomous Driving M Cătălina Stoian, E Giunchiglia, T Lukasiewicz arXiv e-prints, arXiv: 2402.11362, 2024 | 3* | 2024 |
| CCN+: A neuro-symbolic framework for deep learning with requirements E Giunchiglia, A Tatomir, MC Stoian, T Lukasiewicz International Journal of Approximate Reasoning, 109124, 2024 | 2 | 2024 |
| How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data MC Stoian, S Dyrmishi, M Cordy, T Lukasiewicz, E Giunchiglia Proceedings of the 12th International Conference on Learning Representations …, 2024 | 1 | 2024 |
| To TTP or not to TTP? Exploiting TTPs to improve ML-based malware detection Y Sharma, E Giunchiglia, S Birnbach, I Martinovic IEEE International Conference on Cyber Security and Resilience (CSR), 8-15, 2023 | 1 | 2023 |
| Deep learning with hard logical constraints E Giunchiglia University of Oxford, 2022 | 1 | 2022 |
| PiShield: A NeSy Framework for Learning with Requirements MC Stoian, A Tatomir, T Lukasiewicz, E Giunchiglia arXiv preprint arXiv:2402.18285, 2024 | | 2024 |
| PiShield: A NeSy Framework for Learning with Requirements M Cătălina Stoian, A Tatomir, T Lukasiewicz, E Giunchiglia arXiv e-prints, arXiv: 2402.18285, 2024 | | 2024 |
Thomas LukasiewiczVienna University of Technology, Austria; University of Oxford, UKVerified email at tuwien.ac.at
