| Graphframex: Towards systematic evaluation of explainability methods for graph neural networks K Amara, R Ying, Z Zhang, Z Han, Y Shan, U Brandes, S Schemm, ... arXiv preprint arXiv:2206.09677, 2022 | 45 | 2022 |
| ReforesTree: A dataset for estimating tropical forest carbon stock with deep learning and aerial imagery G Reiersen, D Dao, B Lütjens, K Klemmer, K Amara, A Steinegger, ... Proceedings of the AAAI Conference on Artificial Intelligence 36 (11), 12119 …, 2022 | 22 | 2022 |
| Generative explanations for graph neural network: Methods and evaluations J Chen, K Amara, J Yu, R Ying arXiv preprint arXiv:2311.05764, 2023 | 5 | 2023 |
| Ginx-eval: Towards in-distribution evaluation of graph neural network explanations K Amara, M El-Assady, R Ying arXiv preprint arXiv:2309.16223, 2023 | 4 | 2023 |
| Explaining compound activity predictions with a substructure-aware loss for graph neural networks K Amara, R Rodríguez-Pérez, J Jiménez-Luna Journal of cheminformatics 15 (1), 67, 2023 | 4 | 2023 |
| Nearest neighbor search with compact codes: A decoder perspective K Amara, M Douze, A Sablayrolles, H Jégou Proceedings of the 2022 International Conference on Multimedia Retrieval …, 2022 | 4 | 2022 |
| Syntaxshap: Syntax-aware explainability method for text generation K Amara, R Sevastjanova, M El-Assady arXiv preprint arXiv:2402.09259, 2024 | 3 | 2024 |
| PowerGraph: A power grid benchmark dataset for graph neural networks A Varbella, K Amara, B Gjorgiev, G Sansavini arXiv preprint arXiv:2402.02827, 2024 | 1 | 2024 |
| PowerGraph: A power grid benchmark dataset for graph neural networks K Amara, A Varbella, B Gjorgiev, G Sansavini New Frontiers in Graph Learning (GLFrontiers) Workshop@ NeurIPS 2023, 2023 | 1 | 2023 |
| Challenges and Opportunities in Text Generation Explainability K Amara, R Sevastjanova, M El-Assady World Conference on Explainable Artificial Intelligence, 244-264, 2024 | | 2024 |
| A substructure-aware loss for feature attribution in drug discovery K Amara, R Rodriguez-Perez, JJ Luna | | 2022 |
| VF2 AND GLASGOW: PARALLEL INDUCED SUBGRAPH ISOMORPHISM SOLVERS P Lindenberger, A Unagar, K Amara, CI Hu | | |
| Explaining compound activity predictions with a substructure-aware loss for graph neural networks Supporting information K Amara, R Rodríguez-Pérez, J Jiménez-Luna | | |
