| Explainable machine learning with prior knowledge: an overview K Beckh, S Müller, M Jakobs, V Toborek, H Tan, R Fischer, P Welke, ... arXiv preprint arXiv:2105.10172, 2021 | 33 | 2021 |
| Surrogate model-based explainability methods for point cloud nns H Tan, H Kotthaus Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022 | 17 | 2022 |
| Harnessing prior knowledge for explainable machine learning: An overview K Beckh, S Müller, M Jakobs, V Toborek, H Tan, R Fischer, P Welke, ... 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 450-463, 2023 | 11 | 2023 |
| Visualizing global explanations of point cloud dnns H Tan Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 6 | 2023 |
| Explainability-aware one point attack for point cloud neural networks H Tan, H Kotthaus Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 6 | 2023 |
| Maximum entropy baseline for integrated gradients H Tan 2023 International Joint Conference on Neural Networks (IJCNN), 1-8, 2023 | 2 | 2023 |
| Fractual projection forest: Fast and explainable point cloud classifier H Tan Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 2 | 2023 |
| Flow AM: Generating Point Cloud Global Explanations by Latent Alignment H Tan arXiv preprint arXiv:2404.18760, 2024 | | 2024 |
| DAM: Diffusion Activation Maximization for 3D Global Explanations H Tan arXiv preprint arXiv:2401.14938, 2024 | | 2024 |
| The Generalizability of Explanations H Tan 2023 International Joint Conference on Neural Networks (IJCNN), 2023 | | 2023 |
