| Variational autoencoders for cancer data integration: design principles and computational practice N Simidjievski, C Bodnar, I Tariq, P Scherer, H Andres Terre, Z Shams, ... Frontiers in genetics 10, 1205, 2019 | 121 | 2019 |
| Concept embedding models: Beyond the accuracy-explainability trade-off M Espinosa Zarlenga, P Barbiero, G Ciravegna, G Marra, F Giannini, ... Advances in Neural Information Processing Systems 35, 21400-21413, 2022 | 43 | 2022 |
| Efficient decompositional rule extraction for deep neural networks ME Zarlenga, Z Shams, M Jamnik arXiv preprint arXiv:2111.12628, 2021 | 24 | 2021 |
| Marleme: A multi-agent reinforcement learning model extraction library D Kazhdan, Z Shams, P Lio 2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020 | 16 | 2020 |
| Towards robust metrics for concept representation evaluation ME Zarlenga, P Barbiero, Z Shams, D Kazhdan, U Bhatt, A Weller, ... Proceedings of the AAAI Conference on Artificial Intelligence 37 (10), 11791 …, 2023 | 15 | 2023 |
| Argumentation-based reasoning about plans, maintenance goals, and norms Z Shams, MD Vos, N Oren, J Padget ACM Transactions on Autonomous and Adaptive Systems (TAAS) 14 (3), 1-39, 2020 | 14 | 2020 |
| Human inference beyond syllogisms: an approach using external graphical representations Y Sato, G Stapleton, M Jamnik, Z Shams Cognitive Processing 20 (1), 103-115, 2019 | 12 | 2019 |
| Normative practical reasoning via argumentation and dialogue Z Shams, M De Vos, N Oren, J Padget 25th International Joint Conference on Artificial Intelligence (IJCAI), 2016 …, 2016 | 12 | 2016 |
| REM: an integrative rule extraction methodology for explainable data analysis in healthcare Z Shams, B Dimanov, S Kola, N Simidjievski, HA Terre, P Scherer, ... medRxiv, 2021.01. 25.21250459, 2021 | 11 | 2021 |
| Practical reasoning with norms for autonomous software agents Z Shams, M De Vos, J Padget, WW Vasconcelos Engineering Applications of Artificial Intelligence 65, 388-399, 2017 | 11 | 2017 |
| Reasoning with concept diagrams about antipatterns in ontologies Z Shams, M Jamnik, G Stapleton, Y Sato Intelligent Computer Mathematics: 10th International Conference, CICM 2017 …, 2017 | 9 | 2017 |
| Accessible reasoning with diagrams: from cognition to automation Z Shams, Y Sato, M Jamnik, G Stapleton International conference on theory and application of diagrams, 247-263, 2018 | 7 | 2018 |
| Tabcbm: Concept-based interpretable neural networks for tabular data ME Zarlenga, Z Shams, ME Nelson, B Kim, M Jamnik Transactions on Machine Learning Research, 2023 | 5 | 2023 |
| A two-phase dialogue game for skeptical preferred semantics Z Shams, N Oren Logics in Artificial Intelligence: 15th European Conference, JELIA 2016 …, 2016 | 5 | 2016 |
| Learning to Receive Help: Intervention-Aware Concept Embedding Models ME Zarlenga, KM Collins, K Dvijotham, A Weller, Z Shams, M Jamnik arXiv preprint arXiv:2309.16928, 2023 | 4 | 2023 |
| How Network-based and set-based visualizations aid consistency checking in ontologies Y Sato, G Stapleton, M Jamnik, Z Shams, A Blake Proceedings of the 10th International Symposium on Visual Information …, 2017 | 4 | 2017 |
| On the quality assurance of concept-based representations ME Zarlenga, P Barbiero, Z Shams, D Kazhdan, U Bhatt, M Jamnik | 3 | 2021 |
| Using ontology embeddings for structural inductive bias in gene expression data analysis M Trębacz, Z Shams, M Jamnik, P Scherer, N Simidjievski, HA Terre, ... arXiv preprint arXiv:2011.10998, 2020 | 3 | 2020 |
| Exploring and conceptualising attestation I Oliver, J Howse, G Stapleton, Z Shams, M Jamnik Graph-Based Representation and Reasoning: 24th International Conference on …, 2019 | 3 | 2019 |
| Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases P Scherer, M Trębacz, N Simidjievski, R Viñas, Z Shams, HA Terre, ... Bioinformatics 38 (5), 1320-1327, 2022 | 2 | 2022 |
